I would like to thank many people who left their mark on this dissertation as well as on me as a person. Prof. Bożena Kostek, I could always count on your advice and you were always available to me no matter what time of day or day of the week. Roberto Barra-Chicote, I still remember our discussion in Cambridge on affective computing and empathetic AI, it was 2018. This discussion prompted me to do my Ph.D. research, and you, Roberto, have been with me all this time. Jaime Lorenzo-Trueba, thank you for our frequent discussions about the results of scientific experiments and research plans for the future. Thomas Drugman, thanks to you, my writing in English has improved significantly and the clarity of my publications is now much better. Szymon Zaporowski, you recorded the corpus of non-native speech, which gave me the data to evaluate my pronunciation error detection models. Shira Calamaro, you helped with the linguistic and phonetic parts of the research, and with understanding the nature of pronunciation errors made by non-native speakers. Grzegorz Beringer, your internship at Amazon on pronunciation error detection and our numerous discussions motivated me to take up this topic in my Ph.D. research. Alicja Serafinowicz, you gave me a unique perspective of an English teacher on computer-assisted pronunciation training, and created a list of words that are often mispronounced by your students. Jasha Droppo, you have advised me how important it is to keep the big picture of the research in mind. I still remember your remark about moving not only forward but in the right long-term direction. Gary Cook, Andrew Breen, Mateusz Łajszczak, Adam Nadolski, Jonah Rohnke, Viacheslav Klimkov, and Kayoko Yanagisava thank you for reviewing my publications and thesis, and providing many constructive comments. Thank you to Amazon company for allowing me to use the Amazon EC2 cloud to conduct research experiments and for 5 weeks to work on the final mile of my Ph.D. thesis. I would like to thank my beloved wife Luiza and my sons Kacper, Mateusz, and Tymoteusz. There are no words to express how grateful I am to them. Only they know how much support and dedication they gave me. Finally, I would like to thank my parents who persistently raised me in difficult times.

List of Figures

List of Tables

Language Learning and Speech Processing

CALL     Computer-Assisted Language Learning

CAPT     Computer-Assisted Pronunciation Training

EPI     English Proficiency Index

ESL     English as a Second Language

GOP     Goodness of Pronunciation

L1     Native language

L2     Non-native language

MCD     Mel Cepstral Distortion

MDN     Mispronunciations Detection Network

MDD     Mispronunciation Detection and Diagnosis

MOS     Mean Opinion Score

MUSHRA     MUltiple Stimuli with Hidden Reference and Anchor

PM     Pronunciation Model

PR     Phoneme Recognizer

PRN     Phoneme Recognition Network

TTS     Text-To-Speech

Math, Stats, and Machine Learning

AUC     Area Under the ROC Curve

CDF     Cumulative Density Function

CPT     Conditional Probability Table

XOR     Exclusive OR

EM     Expectation Maximization

FAR     False Acceptance Rate

FNR     False Negative Rate

FP     False Positives

FPR     False Positive Rate

FN     False Negatives

FRR     False Rejection Rate

GMM     Gaussian Mixture Model

GP     Gaussian Process

HMM     Hidden Markov Model

iid     independently and identically distributed

KLD     Kullback–Leibler Divergence

ML     Machine Learning

PDF     Probability Density Function

PGM     Probabilistic Graphical Model

PMF     Probability Mass Function

RBF     Radial Basis Function

TN     True Negatives

TP     True Positives

TPR     True Positive Rate

Deep Learning

A-RNN     Attention-based Recurrent Neural Network

CNN     Convolutional  Neural Network

CTC     Connectionist Temporal Classification

DGP     Deep Gaussian Processes

DNN     Deep Neural Networks

MLP     Multi-Layer Perceptron

NF     Normalizing Flow

RCNN     Recurrent Convolutional Neural Network

ReLU     Rectified Linear Unit

RNN     Recurrent Neural Network

VAE     Variational AutoEncoders

VQ-VAE     Vector-Quantized Variational-Auto-Encoder

Other terms

GUT     Gdansk University of Technology

ITU-R     International Telecommunication Union – Radiocommunication Sector

UNESCO     United Nations Educational, Scientific and Cultural Organization

$x,y$     local variables used across different chapter of the Ph.D. Thesis

$\textbf{x},\textbf{y}$     variables in bold indicate vectors or a list of variables

$f: x \mapsto  y$     a function mapping from $x$ to $y$

$y=f(x)$     a function mapping from $x$ to $y$

$\{1,2,3\}$     a set of three numbers

$(0,1)$     an open interval from 0 to 1, excluding the values of 0 and 1

$[0,1]$     a closed interval from 0 to 1, including the values of 0 and 1

$x\in \mathcal{R}^{D}$     a variable $x$ is a member of a set of $D$-dimensional real numbers

$x \in [0,1]$     a variable $x$ is a member of a closed interval between 0 and 1

$\{1..N\} \backslash i$     a set excluding the $i^{th}$  element

$x^T$     the transpose of the variable

$x \odot y$      element-wise matrix multiplication

($f_1 \circ f_2) (x)$     $f_1(f_2(x))$

$|x-y|$     Euclidean distance

$|K|$     determinant of matrix K

$p(x)$      a probability distribution of the variable x

$p(x,y)$      a joint probability distribution of the variables $x$ and $y$

$p(x|y)$     a conditional probability distribution ($x$ conditioned on $y$)

$x \sim p(x|y)$     a variable follows the probability distribution

$p(y) \sim \int p(x,y)dx$     a marginal distribution over the variable $y$

$p(x|y) \propto p(x)p(y|x)$     the probability distribution p(x|y) is proportional to

$\mu$     the mean value of a probability distribution

$\sigma$     standard deviation of a probability distribution

$\tilde{x}, \tilde{\mu}, \tilde {\sigma_2}$     a variable with tilde corresponds to the posterior value of that variable

$\sigma^2$     variance of a probability distribution

$\mathcal{N}(\mu, \sigma^2)$     Normal (Gaussian) probability distribution

$\mathcal{L}(\theta)$     the likelihood function parametrized by $\theta$

$\mathcal{I}$     identity matrix

$p-value$     the probability value in the null-hypothesis statistical test

$t-test$     a statistical test checking for a significant difference in the two mean values

$tp$     the number of true positives,  $tp \in \mathbb{Z}$

$tn$     the number of true negatives, $tn \in \mathbb{Z}$

$fp$     the number of false positives, $fp \in \mathbb{Z}$

$fn$     the number of false negatives, $fn \in \mathbb{Z}$

$e$     the probability of pronunciation error, $e \in (0,1)$

$t$      a threshold value

$\theta$     trainable parameters of a machine learning model

$\kappa$     an activation function in neural networks

Missing 'biblatex' package
The bibliography requires the 'biblatex' package.

titleArpabebet, accessed on June 2022

titleAmerican Speech-Language-Hearing Association (ASHA), accessed on June 2022

booktitleaccepted to Interspeech 2022
titleCreating New Voices using Normalizing Flows

journaltitlearXiv preprint arXiv:2203.01826, submitted to INTERSPEECH 2022
titleImproving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information

booktitleICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleTransformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment

journaltitleaccepted for publication in Speech Communication Journal on June 17 ‘2022, in print
titleComputer-assisted Pronunciation Training - Speech synthesis is almost all you need

booktitleICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleText-Free Non-Parallel Many-To-Many Voice Conversion Using Normalising Flow

journaltitlearXiv preprint arXiv:2204.13206
titleImproving Multimodal Speech Recognition by Data Augmentation and Speech Representations

booktitleaccepted to Interspeech 2022
titleL2-GEN: A Neural Phoneme Paraphrasing Approach to L2 Speech Synthesis for Mispronunciation Diagnosis

ACM New York, NY

journaltitleACM Transactions on Intelligent Systems and Technology (TIST)
titleAn attentive survey of attention models

SIL International

titleEthnologue: Languages of the World. Twenty-fourth edition. Dallas

booktitleProc. 11th ISCA Speech Synthesis Workshop (SSW 11)
titleEnhancing audio quality for expressive Neural Text-to-Speech

booktitleProc. Interspeech 2021
titleSynthASR: Unlocking Synthetic Data for Speech Recognition

journaltitlearXiv preprint arXiv:2104.08428
titleA Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques

booktitleProc. Interspeech 2021
titleImproving the Expressiveness of Neural Vocoding with Non-Affine Normalizing Flows

journaltitlearXiv preprint arXiv:2106.01415
titleA Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion

booktitleICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleLow-resource expressive text-to-speech using data augmentation

booktitleICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleUniversal neural vocoding with parallel wavenet

booktitleProc. Interspeech 2021
titleDetection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention

booktitleProc. Interspeech 2021
titleWeakly-Supervised Word-Level Pronunciation Error Detection in Non-Native English Speech

booktitleICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleMispronunciation Detection in Non-Native (L2) English with Uncertainty Modeling

booktitleProc. Interspeech 2021
titleDeep Feature Transfer Learning for Automatic Pronunciation Assessment

journaltitlearXiv preprint arXiv:2104.09995
titleReview of end-to-end speech synthesis technology based on deep learning

booktitleProc. Interspeech 2021
titleA Study on Fine-Tuning wav2vec2.0 Model for the Task of Mispronunciation Detection and Diagnosis

Acoustical Society of America

journaltitleThe Journal of the Acoustical Society of America
titleEvaluation of aspiration problems in L2 English pronunciation employing machine learning

International Speech Communication Association

booktitle22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
titleAutomatically Detecting Errors and Disfluencies in Read Speech to Predict Cognitive Impairment in People with Parkinson’s Disease

booktitleProc. 11th ISCA Speech Synthesis Workshop (SSW 11)
titleNon-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech

titleMost common languages used on the internet as of January 2020, by share of internet users

Multidisciplinary Digital Publishing Institute

titleDevelopment and Evaluation of Speech Synthesis System Based on Deep Learning Models

booktitleProc. Interspeech 2021
titleExplore wav2vec 2.0 for Mispronunciation Detection

journaltitlearXiv preprint arXiv:2103.03023
titleEnd-to-End Mispronunciation Detection and Diagnosis From Raw Waveforms

journaltitlearXiv preprint arXiv:2108.13816
titleMaximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech

journaltitleSpeech Communication
titleText-conditioned Transformer for automatic pronunciation error detection

journaltitleJournal of advanced English studies
titleTHE EFFECTS OF CALL (COMPUTER ASSISTED LANGUAGE LEARNING) TOWARD THE STUDENTS’ENGLISH ACHIEVEMENT AND ATTITUDE

journaltitleAmazon Machine Learning Conference, Seattle
titleExtending Goodness of Pronunciation to generate mispronunciationhypotheses for pronunciation assessment in L2-English

journaltitlearXiv preprint arXiv:2005.11902
titleASR-Free Pronunciation Assessment

EF Education First

titleEF English Proficiency Index

journaltitlearXiv preprint arXiv:2010.11439
titleParallel Tacotron: Non-Autoregressive and Controllable TTS

journaltitlearXiv preprint arXiv:2003.06505
titleAutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

journaltitleJournal of Machine Learning Research
titleGluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing.

journaltitleIEEE Transactions on Pattern Analysis and Machine Intelligence
titleNormalizing flows: An introduction and review of current methods

journaltitleIEEE Transactions on Learning Technologies
titleAssessing pronunciation improvement in students of English using a controlled computer-assisted pronunciation tool

booktitle24th IEEE SPA
titleConstructing a Dataset of Speech Recordings with Lombard Effect

booktitleProc. Interspeech 2020
titleAn End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling

Multidisciplinary Digital Publishing Institute

titleEnd-to-end automatic pronunciation error detection based on improved hybrid ctc/attention architecture

journaltitleIEEE/ACM transactions on audio, speech, and language processing
titleUnsupervised speech representation learning using wavenet autoencoders

journaltitleIEEE/ACM Transactions on Audio, Speech, and Language Processing
titleCross-lingual transfer learning of non-native acoustic modeling for pronunciation error detection and diagnosis

Tampere University

titleData Augmentation Techniques for Robust Audio Analysis

journaltitlearXiv preprint arXiv:1904.06037
titleDirect speech-to-speech translation with a sequence-to-sequence model

booktitleProceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval
titleSpeech Error Detection depending on Linguistic Units

journaltitleProc. Interspeech 2019
titleInterpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech

Acoustical Society of America

journaltitleThe Journal of the Acoustical Society of America
titleDeep learning model for automated assessment of lexical stress of non-native English speakers

booktitleICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleEffect of data reduction on sequence-to-sequence neural tts

booktitleICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleCNN-RNN-CTC based end-to-end mispronunciation detection and diagnosis

Taylor & Francis

journaltitleComputer Assisted Language Learning
titleDiagnosing L2 learners’ development through online computerized dynamic assessment

journaltitlearXiv preprint arXiv:1901.08644
titleAblation studies in artificial neural networks

booktitleSecond Workshop on Machine Learning and the Physical Sciences, NeurIPS
titleEmulation of physical processes with emukit

booktitle2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
titleTowards Real-Time Mispronunciation Detection in Kids' Speech

titleASR Inspired Syllable Stress Detection for Pronunciation Evaluation Without Using a Supervised Classifier and Syllable Level Features.

journaltitlearXiv preprint arXiv:1905.09263
titleFastspeech: Fast, robust and controllable text to speech

journaltitlearXiv preprint arXiv:1911.04862
titleAn End-to-end Approach for Lexical Stress Detection based on Transformer

Wiley Online Library

journaltitleMethods in Ecology and Evolution
titleThe area under the precision-recall curve as a performance metric for rare binary events

titleNoise robust goodness of pronunciation measures using teacher's utterance

titleAn Improved Goodness of Pronunciation (GoP) Measure for Pronunciation Evaluation with DNN-HMM System Considering HMM Transition Probabilities.

booktitleProceedings of the 10th Speech Synthesis Workshop (SSW10)
titleSpeech synthesis evaluation—state-of-the-art assessment and suggestion for a novel research program

titleChild Speech Disorder Detection with Siamese Recurrent Network Using Speech Attribute Features.

booktitleProc. Interspeech 2019
titleLibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

titleThe American Speech-Language-Hearing Association (ASHA) - Dysarthria

abstractIntroduction Population aging increases the number of people with dementia. Dementia is a set of symptoms that include memory difficulties, learning difficulties, speech and language difficulties, disorientation in time and space, difficulties in understanding and behavioral changes. Dementia is not part of natural aging and needs to be understood as such and have to be recognized at time to provide adequate support for people with dementia. Aim To present the importance of communication: To present communication difficulties which are the result of dementia; To present adaptations in the way of communicating with people with dementia. Material and methods The article has a descriptive character, and represents a review of the literature dealing with this topic. Results Difficulties in area of language are a common symptom in people with dementia. Those communication difficulties are a consequence of nerve cell failure, and person with dementia should not be blamed of the symptoms that arise. People with dementia show lower results in the area of understanding and verbal expression, repetition, reading and writing. Syntax and phonology remain relatively intact in early stages, but semantic abilities are impaired. Conclusion Communication for people with dementia and with people with dementia for all persons involved in care (including family members, medical staff and therapists, and members of the community) can be very challenging. It is often necessary to adapt the way of communication to avoid stress and negative feelings in a person with dementia. As the disease causing dementia progresses, communication problems are increasing as well. Many times caregivers and therapists are in situations where their communicative behavior (verbal, but also nonverbal) needs to show support, compassion, care, and desire to help.
journaltitleMateria Socio Medica
titleCommunication Difficulties as a Result of Dementia

The Academy of Medical Sciences of Bosnia

journaltitleMateria socio-medica
titleCommunication difficulties as a result of dementia

journaltitlearXiv preprint arXiv:1809.03006
titlePerformance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology

journaltitlePattern Recognition
titleRecent advances in convolutional neural networks

Korean Society of Speech Sciences

journaltitlePhonetics and Speech Sciences
titleAcoustic analysis of English lexical stress produced by Korean, Japanese and Taiwanese-Chinese speakers

titleExcitation Source Analysis of Dysarthric Speech for Early Stage Detection of Dysarthria

titleEffect of data reduction on sequence-to-sequence neural {TTS}

journaltitleSpeech Communication
titleAutomatic lexical stress and pitch accent detection for L2 English speech using multi-distribution deep neural networks

journaltitlearXiv preprint arXiv:1811.06292
titleTowards achieving robust universal neural vocoding

journaltitlearXiv preprint arXiv:1801.00631
titleDeep learning: A critical appraisal

abstractWe develop a generalisation of disentanglement in variational auto-encoders (VAEs)—decomposition of the latent representation—characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior. Decomposition permits disentanglement, i.e. explicit independence between latents, as a special case, but also allows for a much richer class of properties to be imposed on the learnt representation, such as sparsity, clustering, independent subspaces, or even intricate hierarchical dependency relationships. We show that the $$-VAE varies from the standard VAE predominantly in its control of latent overlap and that for the standard choice of an isotropic Gaussian prior, its objective is invariant to rotations of the latent representation. Viewed from the decomposition perspective, breaking this invariance with simple manipulations of the prior can yield better disentanglement with little or no detriment to reconstructions. We further demonstrate how other choices of prior can assist in producing different decompositions and introduce an alternative training objective that allows the control of both decomposition factors in a principled manner.
titleDisentangling Disentanglement in Variational Auto-Encoders

booktitle2018 IEEE Spoken Language Technology Workshop (SLT)
titleComprehensive evaluation of statistical speech waveform synthesis

booktitleInterspeech 2018, 19th Annual Conference of the International Speech Communication Association, Hyderabad, India, 2-6 September 2018.
titleDysarthric Speech Classification Using Glottal Features Computed from Non-words, Words and Sentences

booktitleInternational conference on machine learning
titleParallel wavenet: Fast high-fidelity speech synthesis

titleTowards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron

booktitleinternational conference on machine learning
titleTowards end-to-end prosody transfer for expressive speech synthesis with tacotron

booktitleInterspeech 2018, 19th Annual Conference of the International Speech Communication Association, Hyderabad, India, 2-6 September 2018.
titleA Multitask Learning Approach to Assess the Dysarthria Severity in Patients with Parkinson's Disease

World Economic Forum

titleSpeaking more than one language can boost economic growth

titlePaired phone-posteriors approach to esl pronunciation quality assessment

titleLearning latent representations for style control and transfer in end-to-end speech synthesis

journaltitlePerception Sensing Instrumentation Lab
titleL2-ARCTIC: A non-native English speech corpus

booktitle2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)
titleThe limitations of data perturbation for ASR of learner data in under-resourced languages

abstractPURPOSE: Posterior fossa arachnoid cysts (PFAC) are mostly considered as benign lesions of the cerebellum. Although many studies have shown the major role of the cerebellum in modulating movement, language, cognition, and social interaction, there are few studies on the cognitive impact and surgical decompression of PFAC. METHODS: We present the cases of two brothers successively diagnosed with PFAC and neuropsychological delay. After multidisciplinary discussion with the boys' parents, it was decided to drain these lesions. Clinical signs, cerebral images, and neuropsychological status were assessed on admission and then 1 and 3 years after surgery. RESULTS: At presentation, both children had mild cerebellar signs, associated with cognitive and visual-motor impairments and academic regression. CT scans revealed retrovermian cysts, which were shunted. Post-operatively, both brothers demonstrated improved visual-motor skills and behavior. At follow-up, we observed disappearance of dysarthria and academic delay and significant improvement in cognition especially at the intelligence scale and in language. Fine motor skills had improved but remained slower than the average and writing skills appeared limited. CONCLUSION: Except for PFAC which impair cerebrospinal fluid circulation or which are responsible for a significant mass effect, most PFAC are usually considered as "asymptomatic" and do not require surgical treatment. The two cases reported herein suggest that these lesions might be responsible for some associated but potentially reversible neuropsychological impairment. In the future, clinical assessment should include neuropsychological evaluation to help inform decision for surgical decompression in these children with PFAC.
journaltitleChild's Nervous System
titleNeuropsychological improvement after posterior fossa arachnoid cyst drainage
Arachnoid cyst,Children,Cognition,Motor skill,Posterior fossa

journaltitleChild's Nervous System
titleNeuropsychological improvement after posterior fossa arachnoid cyst drainage

journaltitleJournal of Intelligent Information Systems
titleAn audio-visual corpus for multimodal automatic speech recognition

abstractDysarthria is a motor speech disorder that impacts verbal articulation and co-ordination, resulting in slow, slurred and imprecise speech. Automated classification of dysarthria subtypes and severities could provide a useful clinical tool in assessing the onset and progress in treatment. This study represents a pilot project to train models to detect the presence of dysarthria in continuous speech. Subsets of the Universal Access Research Dataset (UA-Speech) and the Atlanta Motor Speech Disorders Corpus (AMSDC) database were utilized in a cross-database training strategy (training on UA-Speech / testing on AMSDC) to distinguish speech with and without dysarthria. In addition to traditional spectral and prosodic features, the current study also includes features based on the Teager Energy Operator (TEO) and the glottal waveform. Baseline results on the UA-Speech dataset maximize wordand participant-level accuracies at 75.3% and 92.9% using prosodic features. However, the cross-training of UA-Speech tested on the AMSDC maximize word- and participant-level accuracies at 71.3% and 90% based on a TEO feature. The results of this pilot study reinforce consideration of dysarthria subtypes in cross-dataset training as well as highlight additional features that may be sensitive to the presence of dysarthria in continuous speech. Copyright © 2017 ISCA.
booktitleProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
titleCross-database models for the classification of dysarthria presence
Cross-database training,Dysarthria,Glottal features,Speech disorders,Teager

titleUnsupervised Learning of Disentangled and Interpretable Representations from Sequential Data

titleControllable Text Generation

Wiley Online Library

journaltitleForeign Language Annals
titleThe benefits of multilingualism to the personal and professional development of residents of the US

titleMontreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi.

Princeton University Press

titleCardano: The gambling scholar

journaltitleResearch in Language
titleEnglish word stress in Polish learners speech production and metacompetence

titleBias and Statistical Significance in Evaluating Speech Synthesis with Mean Opinion Scores.

booktitleInterspeech 2017, 18th Annual Conference of the International Speech Communication Association, Stockholm, Sweden, August 20-24, 2017
titleInterpretable Objective Assessment of Dysarthric Speech Based on Deep Neural Networks

journaltitleAdvances in Neural Information Processing Systems
titleNeural discrete representation learning

titleAttention Is All You Need

booktitleAdvances in neural information processing systems
titleAttention is all you need

titleTacotron: {A} Fully End-to-End Text-To-Speech Synthesis Model

journaltitlearXiv preprint arXiv:1703.10135
titleTacotron: Towards end-to-end speech synthesis

booktitle2016 IEEE international conference on acoustics, speech and signal processing (ICASSP)
titleEnd-to-end attention-based large vocabulary speech recognition

John Wiley & Sons, Ltd

journaltitleCochrane database of systematic reviews
titleSpeech and language therapy for aphasia following stroke

abstractIn just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs have already shown promise in generating many kinds of complicated data, including handwritten digits, faces, house numbers, CIFAR images, physical models of scenes, segmentation, and predicting the future from static images. This tutorial introduces the intuitions behind VAEs, explains the mathematics behind them, and describes some empirical behavior. No prior knowledge of variational Bayesian methods is assumed.
annotationcite arxiv:1606.05908
titleTutorial on Variational Autoencoders
VAE sgd

MIT press

titleDeep learning

booktitle2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleMachine translation based data augmentation for cantonese keyword spotting

Wolters Kluwer–Medknow Publications

journaltitleNeural Regeneration Research
titleSpeech and language therapy for aphasia following subacute stroke

Massachusetts Institute of Technology

titleLanguage-independent methods for computer-assisted pronunciation training

journaltitleIEEE/ACM Transactions on Audio, Speech, and Language Processing
titleMispronunciation detection and diagnosis in l2 English speech using multidistribution deep neural networks

abstractThis paper introduces a new British English speech database, named the homeService corpus, which has been gathered as part of the homeService project. This project aims to help users with speech and motor disabilities to operate their home appliances using voice commands. The audio recorded during such interactions consists of realistic data of speakers with severe dysarthria. The majority of the homeService corpus is recorded in real home environments where voice control is often the normal means by which users interact with their devices. The collection of the corpus is motivated by the shortage of realistic dysarthric speech corpora available to the scientific community. Along with the details on how the data is organised and how it can be accessed, a brief description of the framework used to make the recordings is provided. Finally, the performance of the homeService automatic recogniser for dysarthric speech trained with single-speaker data from the corpus is provided as an initial baseline. Access to the homeService corpus is provided through the dedicated web page at http://mini.dcs.shef.ac.UK/resources/homeservice-corpus/. This will also have the most updated description of the data. At the time of writing the collection process is still ongoing.
booktitleProceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
titleA framework for collecting realistic recordings of dysarthric speech - The homeService corpus
Domestic appliances,Speech,Speech recognition,Webs

journaltitlearXiv preprint arXiv:1611.09207
titleAutoMOS: Learning a non-intrusive assessor of naturalness-of-speech

booktitleProceedings of the 18th International Conference on Information Integration and Web-based Applications and Services
titleNon-native English speakers' speech correction, based on domain focused document

titleAutomatic Classification of Lexical Stress in English and Arabic Languages Using Deep Learning.

UNESCO Paris

journaltitlePolicy Paper 24 of Global Education Monitoring Report
titleIf you don’t understand, how can you learn?

titleUniversity physics Volume 1

booktitle2016 international conference on digital image computing: techniques and applications (DICTA)
titleUnderstanding data augmentation for classification: when to warp?

booktitleInternational Conference on Learning and Collaboration Technologies
titleAutomatic pronunciation error detection and feedback generation for call applications

titleOne in three people born in 2015 will develop dementia, new analysis shows

titleGenerating Sentences from a Continuous Space

titleMXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

journaltitlearXiv preprint arXiv:1512.01274
titleMxnet: A flexible and efficient machine learning library for heterogeneous distributed systems

booktitleAdvances in neural information processing systems
titleAttention-based models for speech recognition

journaltitleSpeech Communication
titleClassification of lexical stress using spectral and prosodic features for computer-assisted language learning systems

journaltitleInvestigating English Pronunciation
titleTrends and directions in computer-assisted pronunciation training

Acoustical Society of America

journaltitleThe Journal of the Acoustical Society of America
titleViSQOLAudio: An objective audio quality metric for low bitrate codecs

Academy & Industry Research Collaboration Center (AIRCC)

journaltitleInternational journal of data mining & knowledge management process
titleA review on evaluation metrics for data classification evaluations

American Association for the Advancement of Science

titleHuman-level concept learning through probabilistic program induction

Nature Publishing Group

titleDeep learning

booktitle2015 IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleAutomatic assessment of English learner pronunciation using discriminative classifiers

booktitle2015 IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleLibrispeech: an asr corpus based on public domain audio books

titleLearning Phrase Representations using {RNN} Encoder-Decoder for Statistical Machine Translation

journaltitlearXiv preprint arXiv:1406.1078
titleLearning phrase representations using RNN encoder-decoder for statistical machine translation

journaltitlearXiv preprint arXiv:1412.1602
titleEnd-to-end continuous speech recognition using attention-based recurrent NN: First results

journaltitleComputer Speech and Language
titleGlottal Source Processing: from Analysis to Applications

titleThe Kernel cookbook: Advice on covariance functions, accessed on June 2022

Taylor & Francis

journaltitleComputer assisted language learning
titleTechnologies for foreign language learning: A review of technology types and their effectiveness

abstractPURPOSE: The purpose of this study was to determine the extent to which vowel metrics are capable of distinguishing healthy from dysarthric speech and among different forms of dysarthria.$\backslash$n$\backslash$nMETHOD: A variety of vowel metrics were derived from spectral and temporal measurements of vowel tokens embedded in phrases produced by 45 speakers with dysarthria and 12 speakers with no history of neurological disease. Via means testing and discriminant function analysis (DFA), the acoustic metrics were used to (a) detect the presence of dysarthria and (b) classify the dysarthria subtype.$\backslash$n$\backslash$nRESULTS: Significant differences between dysarthric and healthy control speakers were revealed for all vowel metrics. However, the results of the DFA demonstrated some metrics (particularly metrics that capture vowel distinctiveness) to be more sensitive and specific predictors of dysarthria. Only the vowel metrics that captured slope of the second formant (F2) demonstrated between-group differences across the dysarthrias. However, when subjected to DFA, these metrics proved unreliable classifiers of dysarthria subtype.$\backslash$n$\backslash$nCONCLUSION: The results of these analyses suggest that some vowel metrics may be useful clinically for the detection of dysarthria but may not be reliable indicators of dysarthria subtype using the current dysarthria classification scheme.
journaltitleJournal of Speech Language and Hearing Research
titleVowel Acoustics in Dysarthria: Speech Disorder Diagnosis and Classification

journaltitleJournal of Machine Learning Research
titleDropout: A Simple Way to Prevent Neural Networks from Overfitting

booktitleAdvances in neural information processing systems
titleSequence to sequence learning with neural networks

Cambridge University Press

titleA history of the mathematical theory of probability

booktitleArtificial intelligence and statistics
titleDeep gaussian processes

booktitle2013 IEEE Intl. conference on acoustics, speech and signal processing
titleSpeech recognition with deep recurrent neural networks

titlePronunciation assessment via a comparison-based system

titleCALL dimensions: Options and issues in computer-assisted language learning

titleLexical stress detection for L2 English speech using deep belief networks.

journaltitlearXiv preprint arXiv:1301.2294
titleExpectation propagation for approximate Bayesian inference

Cambridge University Press

titleBayesian filtering and smoothing

journaltitleInternational Journal of Computer Science Issues (IJCSI)
titleRandom forests and decision trees

Cengage Learning

titleLinguistics for everyone: An introduction

abstractObjective measurement of dysarthric speech intelligibility can assist clinicians in the diagnosis of speech disorder severity as well as in the evaluation of dysarthria treatments. In this paper, several objective measures are proposed and tested as correlates of subjective intelligibility. More specifically, the kurtosis of the linear prediction residual is proposed as a measure of vocal source excitation oddity. Additionally, temporal perturbations resultant from imprecise articulation and atypical speech rates are characterized by short- and long-term temporal dynamics measures, which in turn, are based on log-energy dynamics and on an auditory-inspired modulation spectral signal representation, respectively. Motivated by recent insights in the communication disorders literature, a composite measure is developed based on linearly combining a salient subset of the proposed measures with conventional prosodic parameters. Experiments with the publicly-available 'Universal Access' database of spastic dysarthric speech (10 patient speakers; 300 words spoken in isolation, per speaker) show that the proposed composite measure can achieve correlation with subjective intelligibility ratings as high as 0.97; thus the measure can serve as an accurate indicator of dysarthric speech intelligibility. © 2011 Elsevier B.V. All rights reserved.
journaltitleSpeech Communication
titleCharacterization of atypical vocal source excitation, temporal dynamics and prosody for objective measurement of dysarthric word intelligibility
Dysarthria,Intelligibility,Linear prediction,Temporal dynamics,Vocal source excitation

Karger Publishers

journaltitleDementia and geriatric cognitive disorders extra
titleSpeech therapy in primary progressive aphasia: a pilot study

booktitleSupervised Sequence Labelling with Recurrent Neural Networks
titleConnectionist temporal classification

Digital Codex LLC

isbn0985978805, 9780985978808
titleWebRTC: APIs and RTCWEB Protocols of the HTML5 Real-Time Web

MIT press

titleMachine learning: a probabilistic perspective

abstractThis paper describes the acquisition of a new database of dysarthric speech in terms of aligned acoustics and articulatory data. This database currently includes data from seven individuals with speech impediments caused by cerebral palsy or amyotrophic lateral sclerosis and age- and gender-matched control subjects. Each of the individuals with speech impediments are given standardized assessments of speech-motor function by a speech-language pathologist. Acoustic data is obtained by one head-mounted and one directional microphone. Articulatory data is obtained by electromagnetic articulography, which allows the measurement of the tongue and other articulators during speech, and by 3D reconstruction from binocular video sequences. The stimuli are obtained from a variety of sources including the TIMIT database, lists of identified phonetic contrasts, and assessments of speech intelligibility. This paper also includes some analysis as to how dysarthric speech differs from non-dysarthric speech according to features such as length of phonemes, and pronunciation errors.
journaltitleLanguage Resources and Evaluation
titleThe TORGO database of acoustic and articulatory speech from speakers with dysarthria

abstractIn this paper, automatic dysarthria severity classification is explored as a tool to advance objective intelligibility prediction of spastic dysarthric speech. A Mahalanobis distance-based discriminant analysis classifier is developed based on a set of acoustic features formerly proposed for intelligibility prediction and voice pathology assessment. Feature selection is used to sift salient features for both the disorder severity classification and intelligibility prediction tasks. Experimental results show that a two-level severity classifier combined with a 9-dimensional intelligibility prediction mapping can achieve 0.92 correlation and 12.52 root-mean-square error with subjective intelligibility ratings. The effects of classification errors on intelligibility accuracy are also explored and shown to be insignificant.
titleAutomated Dysarthria Severity Classification for Improved Objective Intelligibility Assessment of Spastic Dysarthric Speech.

abstractIn this invited paper, we overview the clinical applications of speech synthesis technologies and explain a few selected researches. We also introduce the University of Edinburgh's new project “Voice Banking and reconstruction” for patients with degenerative diseases, such as motor neurone disease and Parkinson's disease and show how speech synthesis technologies can improve the quality of life for the patients.
journaltitleAcoustical Science and Technology
titleSpeech synthesis technologies for individuals with vocal disabilities: Voice banking and reconstruction
family=Ahmad Khan,

abstractThis case study describes the generation of a synthetic voice resembling that of an individual before she underwent a laryngectomy. Recordings of this person (6-7 min) speaking prior to the operation were used to create the voice. Synthesis was based on statistical speech models and this method allows models pre-trained on many speakers to be adapted to resemble an individual voice. The results of a listening test in which participants were asked to judge the similarity of the synthetic voice to the pre-operation (target) voice are reported. Members of the patient's family were asked to make a similar judgment. These experiments show that, for most listeners, the voice is quite convincing despite the low quality and small quantity of adaptation data.
booktitleAAC: Augmentative and Alternative Communication
isbn1477-3848 (Electronic)$\backslash$r0743-4618 (Linking)
titleReconstructing the voice of an individual following laryngectomy
Laryngectomy,Speech synthesis,Voice reconstruction

booktitleINTERSPEECH 2011, 12th Annual Conference of the International Speech Communication Association, Florence, Italy, August 27-31, 2011
titleContext-Dependent Duration Modeling with Backoff Strategy and Look-Up Tables for Pronunciation Assessment and Mispronunciation Detection

American Association for the Advancement of Science

titleQuantitative analysis of culture using millions of digitized books

journaltitleProc. APSIPA ASC
titleAutomatic lexical stress detection using acoustic features for computer assisted language learning

booktitle2010 7th Intl. Symposium on Chinese Spoken Language Processing
titleAutomatic lexical stress detection for Chinese learners' of English

seriesJMLR Proceedings
titleUnderstanding the difficulty of training deep feedforward neural networks.

booktitle2010 7th Intl. Symposium on Chinese Spoken Language Processing
titleCapturing L2 segmental mispronunciations with joint-sequence models in computer-aided pronunciation training (CAPT)

Cambridge university press

titleModeling and reasoning with Bayesian networks

booktitleIntl. Workshop on Speech and Language Technology in Education
titleImplementation of an extended recognition network for mispronunciation detection and diagnosis in computer-assisted pronunciation training

Upper Saddle River, NJ, USA

Prentice-Hall, Inc.

titleSpeech and Language Processing (2Nd Edition)

MIT press

titleProbabilistic graphical models: principles and techniques

booktitleProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
titleCombining neural network and rule-based systems for dysarthria diagnosis

abstractThis paper describes a database of dysarthric speech produced by 19 speakers with cerebral palsy. Speech materials consist of 765 isolated words per speaker: 300 distinct uncommon words and 3 repetitions of digits, computer commands, radio alphabet and common words. Data is recorded through an 8-microphone array and one digital video camera. Our database provides a fundamental resource for automatic speech recognition development for people with neuromotor disability. Research on articulation errors in dysarthria will benefit clinical treatments and contribute to our knowledge of neuromotor mechanisms in speech production. Data files are available via secure ftp upon request.
titleDysarthric Speech Database for Universal Access Research
Cerebral palsy,Dysarthria,Speech recognition

journaltitleYulgokbooks, Korea
titleIntroduction to statistics

Taylor & Francis

journaltitleComputer Assisted Language Learning
titleThe effectiveness of computer assisted pronunciation training for foreign language learning by children

journaltitleEncyclopedia of measurement and statistics
titleBinomial distribution: Binomial and sign tests

booktitle2007 Intl. Conference on Computational Intelligence and Security Workshops (CISW 2007)
titleUsing nonlinear features in automatic English lexical stress detection

Wiley Online Library

journaltitleWiley encyclopedia of clinical trials
titleWilcoxon signed-rank test

journaltitleMachine learning
titlePattern recognition

titlePraat: doing phonetics by computer

journaltitleEnglish Phonetics and Phonology
titleThe production of speech sounds

MIT press Cambridge, MA

titleGaussian processes for machine learning

Wiley Online Library

journaltitleTESOL quarterly
titleIntelligibility and the listener: The role of lexical stress

booktitleFifth ISCA workshop on speech synthesis
titleThe CMU Arctic speech databases

titlePronunciation assessment based upon the phonological distortions observed in language learners' utterances

The HIT Centre-Humanities Information Technologies Research Programme

journaltitleICAME Journal: Intl. Computer Archive of Modern and Medieval English Journal
titleThe ISLE corpus: Italian and German spoken learner's English

journaltitleIEEE Information Theory Society Newsletter
titleHidden Markov models and the Baum-Welch algorithm

journaltitleSpeech Communication
titleRobustness to telephone handset distortion in speaker recognition by discriminative feature design

journaltitleSpeech communication
titlePhone-level pronunciation scoring and assessment for interactive language learning

journaltitleMachine learning
titleAn introduction to variational methods for graphical models

booktitle1997 IEEE international conference on acoustics, speech, and signal processing
titleAutomatic pronunciation scoring for language instruction

journaltitleIEEE Signal processing magazine
titleThe expectation-maximization algorithm

journaltitleJournal of Phonetics
titleStress shift and early pitch accent placement in lexical items in American English

titleDARPA TIMIT acoustic-phonetic continous speech corpus CD-ROM. NIST speech disc 1-1.1

booktitlePhonetics and Phonology of Speaking Styles
titleAcoustic and lexical vowel reduction

Annual Reviews 4139 El Camino Way; PO Box 10139; Palo Alto; CA 94303-0139; USA

journaltitleAnnual review of neuroscience
titleThe attention system of the human brain

abstractIn this paper, we present an algorithm to estimate a signal from its modified short-time Fourier transform (STFT). This algorithm is computationally simple and is obtained by minimizing the mean squared error between the STFT of the estimated signal and the modified STFT. Using this algorithm, we also develop an iterative algorithm to estimate a signal from its modified STFT magnitude. The iterative algorithm is shown to decrease, in each iteration, the mean squared error between the STFT magnitude of the estimated signal and the modified STFT magnitude. The major computation involved in the iterative algorithm is the discrete Fourier transform (DFT) computation, and the algorithm appears to be real-time implementable with current hardware technology. The algorithm developed in this paper has been applied to the time-scale modification of speech. The resulting system generates very high-quality speech, and appears to be better in performancc than any existing metho
isbn0096-3518 VO - 32
journaltitleIEEE Transactions on Acoustics, Speech, and Signal Processing
titleSignal Estimation from Modified Short-Time Fourier Transform

Sage Publications Sage CA: Thousand Oaks, CA

journaltitleLanguage and Speech
titleLinking as a marker of fluent speech

Englewood Cliffs: Prentice Hall

titleDigital Processing of Speech Signals
dsp speech

journaltitleJournal of molecular biology
titleA general method applicable to the search for similarities in the amino acid sequence of two proteins

journaltitleProceedings of the IRE
titlePerceptron simulation experiments

journaltitleEncyclopedia of measurement and statistics
titleBinomial distribution: Binomial and sign tests
family=Ahmad Khan,

abstractThis case study describes the generation of a synthetic voice resembling that of an individual before she underwent a laryngectomy. Recordings of this person (6-7 min) speaking prior to the operation were used to create the voice. Synthesis was based on statistical speech models and this method allows models pre-trained on many speakers to be adapted to resemble an individual voice. The results of a listening test in which participants were asked to judge the similarity of the synthetic voice to the pre-operation (target) voice are reported. Members of the patient's family were asked to make a similar judgment. These experiments show that, for most listeners, the voice is quite convincing despite the low quality and small quantity of adaptation data.
booktitleAAC: Augmentative and Alternative Communication
isbn1477-3848 (Electronic)$\backslash$r0743-4618 (Linking)
titleReconstructing the voice of an individual following laryngectomy
Laryngectomy,Speech synthesis,Voice reconstruction

booktitleInternational Conference on Learning and Collaboration Technologies
titleAutomatic pronunciation error detection and feedback generation for call applications

journaltitleInternational Journal of Computer Science Issues (IJCSI)
titleRandom forests and decision trees

titleOne in three people born in 2015 will develop dementia, new analysis shows

titleArpabebet, accessed on June 2022

titleThe American Speech-Language-Hearing Association (ASHA) - Dysarthria

titleAmerican Speech-Language-Hearing Association (ASHA), accessed on June 2022

journaltitleJournal of advanced English studies
titleTHE EFFECTS OF CALL (COMPUTER ASSISTED LANGUAGE LEARNING) TOWARD THE STUDENTS’ENGLISH ACHIEVEMENT AND ATTITUDE

The HIT Centre-Humanities Information Technologies Research Programme

journaltitleICAME Journal: Intl. Computer Archive of Modern and Medieval English Journal
titleThe ISLE corpus: Italian and German spoken learner's English

booktitle2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)
titleThe limitations of data perturbation for ASR of learner data in under-resourced languages

booktitle2016 IEEE international conference on acoustics, speech and signal processing (ICASSP)
titleEnd-to-end attention-based large vocabulary speech recognition

abstractIntroduction Population aging increases the number of people with dementia. Dementia is a set of symptoms that include memory difficulties, learning difficulties, speech and language difficulties, disorientation in time and space, difficulties in understanding and behavioral changes. Dementia is not part of natural aging and needs to be understood as such and have to be recognized at time to provide adequate support for people with dementia. Aim To present the importance of communication: To present communication difficulties which are the result of dementia; To present adaptations in the way of communicating with people with dementia. Material and methods The article has a descriptive character, and represents a review of the literature dealing with this topic. Results Difficulties in area of language are a common symptom in people with dementia. Those communication difficulties are a consequence of nerve cell failure, and person with dementia should not be blamed of the symptoms that arise. People with dementia show lower results in the area of understanding and verbal expression, repetition, reading and writing. Syntax and phonology remain relatively intact in early stages, but semantic abilities are impaired. Conclusion Communication for people with dementia and with people with dementia for all persons involved in care (including family members, medical staff and therapists, and members of the community) can be very challenging. It is often necessary to adapt the way of communication to avoid stress and negative feelings in a person with dementia. As the disease causing dementia progresses, communication problems are increasing as well. Many times caregivers and therapists are in situations where their communicative behavior (verbal, but also nonverbal) needs to show support, compassion, care, and desire to help.
journaltitleMateria Socio Medica
titleCommunication Difficulties as a Result of Dementia

The Academy of Medical Sciences of Bosnia

journaltitleMateria socio-medica
titleCommunication difficulties as a result of dementia

booktitlePhonetics and Phonology of Speaking Styles
titleAcoustic and lexical vowel reduction

journaltitleAmazon Machine Learning Conference, Seattle
titleExtending Goodness of Pronunciation to generate mispronunciationhypotheses for pronunciation assessment in L2-English

booktitleaccepted to Interspeech 2022
titleCreating New Voices using Normalizing Flows

journaltitleMachine learning
titlePattern recognition

titlePraat: doing phonetics by computer

journaltitlearXiv preprint arXiv:1809.03006
titlePerformance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology

titleGenerating Sentences from a Continuous Space

John Wiley & Sons, Ltd

journaltitleCochrane database of systematic reviews
titleSpeech and language therapy for aphasia following stroke

booktitleProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
titleCombining neural network and rule-based systems for dysarthria diagnosis

ACM New York, NY

journaltitleACM Transactions on Intelligent Systems and Technology (TIST)
titleAn attentive survey of attention models

booktitle2010 7th Intl. Symposium on Chinese Spoken Language Processing
titleAutomatic lexical stress detection for Chinese learners' of English

booktitle2007 Intl. Conference on Computational Intelligence and Security Workshops (CISW 2007)
titleUsing nonlinear features in automatic English lexical stress detection

titleMXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

journaltitlearXiv preprint arXiv:1512.01274
titleMxnet: A flexible and efficient machine learning library for heterogeneous distributed systems

journaltitlearXiv preprint arXiv:2005.11902
titleASR-Free Pronunciation Assessment

titleLearning Phrase Representations using {RNN} Encoder-Decoder for Statistical Machine Translation

journaltitlearXiv preprint arXiv:1406.1078
titleLearning phrase representations using RNN encoder-decoder for statistical machine translation

journaltitlearXiv preprint arXiv:1412.1602
titleEnd-to-end continuous speech recognition using attention-based recurrent NN: First results

journaltitleIEEE/ACM transactions on audio, speech, and language processing
titleUnsupervised speech representation learning using wavenet autoencoders

booktitleAdvances in neural information processing systems
titleAttention-based models for speech recognition

abstractPURPOSE: Posterior fossa arachnoid cysts (PFAC) are mostly considered as benign lesions of the cerebellum. Although many studies have shown the major role of the cerebellum in modulating movement, language, cognition, and social interaction, there are few studies on the cognitive impact and surgical decompression of PFAC. METHODS: We present the cases of two brothers successively diagnosed with PFAC and neuropsychological delay. After multidisciplinary discussion with the boys' parents, it was decided to drain these lesions. Clinical signs, cerebral images, and neuropsychological status were assessed on admission and then 1 and 3 years after surgery. RESULTS: At presentation, both children had mild cerebellar signs, associated with cognitive and visual-motor impairments and academic regression. CT scans revealed retrovermian cysts, which were shunted. Post-operatively, both brothers demonstrated improved visual-motor skills and behavior. At follow-up, we observed disappearance of dysarthria and academic delay and significant improvement in cognition especially at the intelligence scale and in language. Fine motor skills had improved but remained slower than the average and writing skills appeared limited. CONCLUSION: Except for PFAC which impair cerebrospinal fluid circulation or which are responsible for a significant mass effect, most PFAC are usually considered as "asymptomatic" and do not require surgical treatment. The two cases reported herein suggest that these lesions might be responsible for some associated but potentially reversible neuropsychological impairment. In the future, clinical assessment should include neuropsychological evaluation to help inform decision for surgical decompression in these children with PFAC.
journaltitleChild's Nervous System
titleNeuropsychological improvement after posterior fossa arachnoid cyst drainage
Arachnoid cyst,Children,Cognition,Motor skill,Posterior fossa

journaltitleChild's Nervous System
titleNeuropsychological improvement after posterior fossa arachnoid cyst drainage

journaltitleJournal of Intelligent Information Systems
titleAn audio-visual corpus for multimodal automatic speech recognition

booktitleArtificial intelligence and statistics
titleDeep gaussian processes

Cambridge university press

titleModeling and reasoning with Bayesian networks

Cengage Learning

titleLinguistics for everyone: An introduction

abstractIn just three years, Variational Autoencoders (VAEs) have emerged as one of the most popular approaches to unsupervised learning of complicated distributions. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs have already shown promise in generating many kinds of complicated data, including handwritten digits, faces, house numbers, CIFAR images, physical models of scenes, segmentation, and predicting the future from static images. This tutorial introduces the intuitions behind VAEs, explains the mathematics behind them, and describes some empirical behavior. No prior knowledge of variational Bayesian methods is assumed.
annotationcite arxiv:1606.05908
titleTutorial on Variational Autoencoders
VAE sgd

journaltitleComputer Speech and Language
titleGlottal Source Processing: from Analysis to Applications

journaltitleIEEE/ACM Transactions on Audio, Speech, and Language Processing
titleCross-lingual transfer learning of non-native acoustic modeling for pronunciation error detection and diagnosis

titleThe Kernel cookbook: Advice on covariance functions, accessed on June 2022

SIL International

titleEthnologue: Languages of the World. Twenty-fourth edition. Dallas

EF Education First

titleEF English Proficiency Index

Tampere University

titleData Augmentation Techniques for Robust Audio Analysis

journaltitlearXiv preprint arXiv:2010.11439
titleParallel Tacotron: Non-Autoregressive and Controllable TTS

journaltitlearXiv preprint arXiv:2003.06505
titleAutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

booktitleProc. 11th ISCA Speech Synthesis Workshop (SSW 11)
titleEnhancing audio quality for expressive Neural Text-to-Speech

abstractObjective measurement of dysarthric speech intelligibility can assist clinicians in the diagnosis of speech disorder severity as well as in the evaluation of dysarthria treatments. In this paper, several objective measures are proposed and tested as correlates of subjective intelligibility. More specifically, the kurtosis of the linear prediction residual is proposed as a measure of vocal source excitation oddity. Additionally, temporal perturbations resultant from imprecise articulation and atypical speech rates are characterized by short- and long-term temporal dynamics measures, which in turn, are based on log-energy dynamics and on an auditory-inspired modulation spectral signal representation, respectively. Motivated by recent insights in the communication disorders literature, a composite measure is developed based on linearly combining a salient subset of the proposed measures with conventional prosodic parameters. Experiments with the publicly-available 'Universal Access' database of spastic dysarthric speech (10 patient speakers; 300 words spoken in isolation, per speaker) show that the proposed composite measure can achieve correlation with subjective intelligibility ratings as high as 0.97; thus the measure can serve as an accurate indicator of dysarthric speech intelligibility. © 2011 Elsevier B.V. All rights reserved.
journaltitleSpeech Communication
titleCharacterization of atypical vocal source excitation, temporal dynamics and prosody for objective measurement of dysarthric word intelligibility
Dysarthria,Intelligibility,Linear prediction,Temporal dynamics,Vocal source excitation

Karger Publishers

journaltitleDementia and geriatric cognitive disorders extra
titleSpeech therapy in primary progressive aphasia: a pilot study

booktitleProc. Interspeech 2021
titleSynthASR: Unlocking Synthetic Data for Speech Recognition

journaltitleSpeech Communication
titleClassification of lexical stress using spectral and prosodic features for computer-assisted language learning systems

Wiley Online Library

journaltitleTESOL quarterly
titleIntelligibility and the listener: The role of lexical stress

journaltitleInvestigating English Pronunciation
titleTrends and directions in computer-assisted pronunciation training

booktitle1997 IEEE international conference on acoustics, speech, and signal processing
titleAutomatic pronunciation scoring for language instruction

journaltitlearXiv preprint arXiv:2203.01826, submitted to INTERSPEECH 2022
titleImproving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information

journaltitlearXiv preprint arXiv:2104.08428
titleA Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques

booktitleProc. Interspeech 2021
titleImproving the Expressiveness of Neural Vocoding with Non-Affine Normalizing Flows

titleDARPA TIMIT acoustic-phonetic continous speech corpus CD-ROM. NIST speech disc 1-1.1

abstractDysarthria is a motor speech disorder that impacts verbal articulation and co-ordination, resulting in slow, slurred and imprecise speech. Automated classification of dysarthria subtypes and severities could provide a useful clinical tool in assessing the onset and progress in treatment. This study represents a pilot project to train models to detect the presence of dysarthria in continuous speech. Subsets of the Universal Access Research Dataset (UA-Speech) and the Atlanta Motor Speech Disorders Corpus (AMSDC) database were utilized in a cross-database training strategy (training on UA-Speech / testing on AMSDC) to distinguish speech with and without dysarthria. In addition to traditional spectral and prosodic features, the current study also includes features based on the Teager Energy Operator (TEO) and the glottal waveform. Baseline results on the UA-Speech dataset maximize wordand participant-level accuracies at 75.3% and 92.9% using prosodic features. However, the cross-training of UA-Speech tested on the AMSDC maximize word- and participant-level accuracies at 71.3% and 90% based on a TEO feature. The results of this pilot study reinforce consideration of dysarthria subtypes in cross-dataset training as well as highlight additional features that may be sensitive to the presence of dysarthria in continuous speech. Copyright © 2017 ISCA.
booktitleProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
titleCross-database models for the classification of dysarthria presence
Cross-database training,Dysarthria,Glottal features,Speech disorders,Teager

seriesJMLR Proceedings
titleUnderstanding the difficulty of training deep feedforward neural networks.

Taylor & Francis

journaltitleComputer assisted language learning
titleTechnologies for foreign language learning: A review of technology types and their effectiveness

booktitleICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleTransformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment

MIT press

titleDeep learning

booktitle2013 IEEE Intl. conference on acoustics, speech and signal processing
titleSpeech recognition with deep recurrent neural networks

booktitleSupervised Sequence Labelling with Recurrent Neural Networks
titleConnectionist temporal classification

abstractIn this paper, we present an algorithm to estimate a signal from its modified short-time Fourier transform (STFT). This algorithm is computationally simple and is obtained by minimizing the mean squared error between the STFT of the estimated signal and the modified STFT. Using this algorithm, we also develop an iterative algorithm to estimate a signal from its modified STFT magnitude. The iterative algorithm is shown to decrease, in each iteration, the mean squared error between the STFT magnitude of the estimated signal and the modified STFT magnitude. The major computation involved in the iterative algorithm is the discrete Fourier transform (DFT) computation, and the algorithm appears to be real-time implementable with current hardware technology. The algorithm developed in this paper has been applied to the time-scale modification of speech. The resulting system generates very high-quality speech, and appears to be better in performancc than any existing metho
isbn0096-3518 VO - 32
journaltitleIEEE Transactions on Acoustics, Speech, and Signal Processing
titleSignal Estimation from Modified Short-Time Fourier Transform

journaltitlePattern Recognition
titleRecent advances in convolutional neural networks

journaltitleJournal of Machine Learning Research
titleGluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing.

booktitleIntl. Workshop on Speech and Language Technology in Education
titleImplementation of an extended recognition network for mispronunciation detection and diagnosis in computer-assisted pronunciation training

journaltitleSpeech Communication
titleRobustness to telephone handset distortion in speaker recognition by discriminative feature design

Sage Publications Sage CA: Thousand Oaks, CA

journaltitleLanguage and Speech
titleLinking as a marker of fluent speech

Acoustical Society of America

journaltitleThe Journal of the Acoustical Society of America
titleViSQOLAudio: An objective audio quality metric for low bitrate codecs

Academy & Industry Research Collaboration Center (AIRCC)

journaltitleInternational journal of data mining & knowledge management process
titleA review on evaluation metrics for data classification evaluations

titleUnsupervised Learning of Disentangled and Interpretable Representations from Sequential Data

titleControllable Text Generation

booktitle2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleMachine translation based data augmentation for cantonese keyword spotting

journaltitlearXiv preprint arXiv:2106.01415
titleA Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion

booktitleICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleLow-resource expressive text-to-speech using data augmentation

journaltitlearXiv preprint arXiv:1904.06037
titleDirect speech-to-speech translation with a sequence-to-sequence model

booktitleICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleUniversal neural vocoding with parallel wavenet

Digital Codex LLC

isbn0985978805, 9780985978808
titleWebRTC: APIs and RTCWEB Protocols of the HTML5 Real-Time Web

journaltitleMachine learning
titleAn introduction to variational methods for graphical models

Korean Society of Speech Sciences

journaltitlePhonetics and Speech Sciences
titleAcoustic analysis of English lexical stress produced by Korean, Japanese and Taiwanese-Chinese speakers

Upper Saddle River, NJ, USA

Prentice-Hall, Inc.

titleSpeech and Language Processing (2Nd Edition)

abstractThis paper describes a database of dysarthric speech produced by 19 speakers with cerebral palsy. Speech materials consist of 765 isolated words per speaker: 300 distinct uncommon words and 3 repetitions of digits, computer commands, radio alphabet and common words. Data is recorded through an 8-microphone array and one digital video camera. Our database provides a fundamental resource for automatic speech recognition development for people with neuromotor disability. Research on articulation errors in dysarthria will benefit clinical treatments and contribute to our knowledge of neuromotor mechanisms in speech production. Data files are available via secure ftp upon request.
titleDysarthric Speech Database for Universal Access Research
Cerebral palsy,Dysarthria,Speech recognition

journaltitleIEEE Transactions on Pattern Analysis and Machine Intelligence
titleNormalizing flows: An introduction and review of current methods

MIT press

titleProbabilistic graphical models: principles and techniques

booktitleProceedings of the 2019 3rd International Conference on Natural Language Processing and Information Retrieval
titleSpeech Error Detection depending on Linguistic Units

booktitleFifth ISCA workshop on speech synthesis
titleThe CMU Arctic speech databases

journaltitleProc. Interspeech 2019
titleInterpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech

booktitleProc. Interspeech 2021
titleDetection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention

Acoustical Society of America

journaltitleThe Journal of the Acoustical Society of America
titleDeep learning model for automated assessment of lexical stress of non-native English speakers

booktitleProc. Interspeech 2021
titleWeakly-Supervised Word-Level Pronunciation Error Detection in Non-Native English Speech

journaltitleaccepted for publication in Speech Communication Journal on June 17 ‘2022, in print
titleComputer-assisted Pronunciation Training - Speech synthesis is almost all you need

booktitleICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleMispronunciation Detection in Non-Native (L2) English with Uncertainty Modeling

Wolters Kluwer–Medknow Publications

journaltitleNeural Regeneration Research
titleSpeech and language therapy for aphasia following subacute stroke

titleExcitation Source Analysis of Dysarthric Speech for Early Stage Detection of Dysarthria

Wiley Online Library

journaltitleForeign Language Annals
titleThe benefits of multilingualism to the personal and professional development of residents of the US

American Association for the Advancement of Science

titleHuman-level concept learning through probabilistic program induction

abstractPURPOSE: The purpose of this study was to determine the extent to which vowel metrics are capable of distinguishing healthy from dysarthric speech and among different forms of dysarthria.$\backslash$n$\backslash$nMETHOD: A variety of vowel metrics were derived from spectral and temporal measurements of vowel tokens embedded in phrases produced by 45 speakers with dysarthria and 12 speakers with no history of neurological disease. Via means testing and discriminant function analysis (DFA), the acoustic metrics were used to (a) detect the presence of dysarthria and (b) classify the dysarthria subtype.$\backslash$n$\backslash$nRESULTS: Significant differences between dysarthric and healthy control speakers were revealed for all vowel metrics. However, the results of the DFA demonstrated some metrics (particularly metrics that capture vowel distinctiveness) to be more sensitive and specific predictors of dysarthria. Only the vowel metrics that captured slope of the second formant (F2) demonstrated between-group differences across the dysarthrias. However, when subjected to DFA, these metrics proved unreliable classifiers of dysarthria subtype.$\backslash$n$\backslash$nCONCLUSION: The results of these analyses suggest that some vowel metrics may be useful clinically for the detection of dysarthria but may not be reliable indicators of dysarthria subtype using the current dysarthria classification scheme.
journaltitleJournal of Speech Language and Hearing Research
titleVowel Acoustics in Dysarthria: Speech Disorder Diagnosis and Classification
ature Publishing Group

titleDeep learning

Massachusetts Institute of Technology

titleLanguage-independent methods for computer-assisted pronunciation training

titlePronunciation assessment via a comparison-based system

journaltitleYulgokbooks, Korea
titleIntroduction to statistics

booktitleProceedings of the International Symposium on the Acquisition of Second Language Speech Concordia Working Papers in Applied Linguistics
titleIntelligibility of English L2: The effects of incorrect word stress placement and incorrect vowel reduction in the speech of French and Italian learners of English

booktitleICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleCNN-RNN-CTC based end-to-end mispronunciation detection and diagnosis

titleCALL dimensions: Options and issues in computer-assisted language learning

booktitleINTERSPEECH 2011, 12th Annual Conference of the International Speech Communication Association, Florence, Italy, August 27-31, 2011
titleContext-Dependent Duration Modeling with Backoff Strategy and Look-Up Tables for Pronunciation Assessment and Mispronunciation Detection

journaltitleSpeech Communication
titleAutomatic lexical stress and pitch accent detection for L2 English speech using multi-distribution deep neural networks

titleLexical stress detection for L2 English speech using deep belief networks.

journaltitleIEEE/ACM Transactions on Audio, Speech, and Language Processing
titleMispronunciation detection and diagnosis in l2 English speech using multidistribution deep neural networks

booktitleProc. Interspeech 2021
titleDeep Feature Transfer Learning for Automatic Pronunciation Assessment

journaltitlearXiv preprint arXiv:1811.06292
titleTowards achieving robust universal neural vocoding

journaltitlearXiv preprint arXiv:1801.00631
titleDeep learning: A critical appraisal

abstractWe develop a generalisation of disentanglement in variational auto-encoders (VAEs)—decomposition of the latent representation—characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior. Decomposition permits disentanglement, i.e. explicit independence between latents, as a special case, but also allows for a much richer class of properties to be imposed on the learnt representation, such as sparsity, clustering, independent subspaces, or even intricate hierarchical dependency relationships. We show that the $$-VAE varies from the standard VAE predominantly in its control of latent overlap and that for the standard choice of an isotropic Gaussian prior, its objective is invariant to rotations of the latent representation. Viewed from the decomposition perspective, breaking this invariance with simple manipulations of the prior can yield better disentanglement with little or no detriment to reconstructions. We further demonstrate how other choices of prior can assist in producing different decompositions and introduce an alternative training objective that allows the control of both decomposition factors in a principled manner.
titleDisentangling Disentanglement in Variational Auto-Encoders

titleMontreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi.

Taylor & Francis

journaltitleComputer Assisted Language Learning
titleDiagnosing L2 learners’ development through online computerized dynamic assessment

booktitleICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleText-Free Non-Parallel Many-To-Many Voice Conversion Using Normalising Flow

booktitle2018 IEEE Spoken Language Technology Workshop (SLT)
titleComprehensive evaluation of statistical speech waveform synthesis

journaltitlearXiv preprint arXiv:1901.08644
titleAblation studies in artificial neural networks

American Association for the Advancement of Science

titleQuantitative analysis of culture using millions of digitized books

titlePronunciation assessment based upon the phonological distortions observed in language learners' utterances

journaltitlearXiv preprint arXiv:1301.2294
titleExpectation propagation for approximate Bayesian inference

journaltitleIEEE Signal processing magazine
titleThe expectation-maximization algorithm

journaltitlearXiv preprint arXiv:2104.09995
titleReview of end-to-end speech synthesis technology based on deep learning

MIT press

titleMachine learning: a probabilistic perspective

booktitleInterspeech 2018, 19th Annual Conference of the International Speech Communication Association, Hyderabad, India, 2-6 September 2018.
titleDysarthric Speech Classification Using Glottal Features Computed from Non-words, Words and Sentences

journaltitleJournal of molecular biology
titleA general method applicable to the search for similarities in the amino acid sequence of two proteins

Taylor & Francis

journaltitleComputer Assisted Language Learning
titleThe effectiveness of computer assisted pronunciation training for foreign language learning by children

booktitle2015 IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleAutomatic assessment of English learner pronunciation using discriminative classifiers

abstractThis paper introduces a new British English speech database, named the homeService corpus, which has been gathered as part of the homeService project. This project aims to help users with speech and motor disabilities to operate their home appliances using voice commands. The audio recorded during such interactions consists of realistic data of speakers with severe dysarthria. The majority of the homeService corpus is recorded in real home environments where voice control is often the normal means by which users interact with their devices. The collection of the corpus is motivated by the shortage of realistic dysarthric speech corpora available to the scientific community. Along with the details on how the data is organised and how it can be accessed, a brief description of the framework used to make the recordings is provided. Finally, the performance of the homeService automatic recogniser for dysarthric speech trained with single-speaker data from the corpus is provided as an initial baseline. Access to the homeService corpus is provided through the dedicated web page at http://mini.dcs.shef.ac.UK/resources/homeservice-corpus/. This will also have the most updated description of the data. At the time of writing the collection process is still ongoing.
booktitleProceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
titleA framework for collecting realistic recordings of dysarthric speech - The homeService corpus
Domestic appliances,Speech,Speech recognition,Webs

journaltitlearXiv preprint arXiv:2204.13206
titleImproving Multimodal Speech Recognition by Data Augmentation and Speech Representations

booktitleInternational conference on machine learning
titleParallel wavenet: Fast high-fidelity speech synthesis

Princeton University Press

titleCardano: The gambling scholar

booktitleSecond Workshop on Machine Learning and the Physical Sciences, NeurIPS
titleEmulation of physical processes with emukit

booktitle2015 IEEE Intl. Conference on Acoustics, Speech and Signal Processing (ICASSP)
titleLibrispeech: an asr corpus based on public domain audio books

journaltitlearXiv preprint arXiv:1611.09207
titleAutoMOS: Learning a non-intrusive assessor of naturalness-of-speech

booktitleProc. Interspeech 2021
titleA Study on Fine-Tuning wav2vec2.0 Model for the Task of Mispronunciation Detection and Diagnosis

Acoustical Society of America

journaltitleThe Journal of the Acoustical Society of America
titleEvaluation of aspiration problems in L2 English pronunciation employing machine learning

booktitle2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
titleTowards Real-Time Mispronunciation Detection in Kids' Speech

journaltitleResearch in Language
titleEnglish word stress in Polish learners speech production and metacompetence

Annual Reviews 4139 El Camino Way; PO Box 10139; Palo Alto; CA 94303-0139; USA

journaltitleAnnual review of neuroscience
titleThe attention system of the human brain

booktitle2010 7th Intl. Symposium on Chinese Spoken Language Processing
titleCapturing L2 segmental mispronunciations with joint-sequence models in computer-aided pronunciation training (CAPT)

Englewood Cliffs: Prentice Hall

titleDigital Processing of Speech Signals
dsp speech

booktitleProceedings of the 18th International Conference on Information Integration and Web-based Applications and Services
titleNon-native English speakers' speech correction, based on domain focused document

titleASR Inspired Syllable Stress Detection for Pronunciation Evaluation Without Using a Supervised Classifier and Syllable Level Features.

journaltitlearXiv preprint arXiv:1905.09263
titleFastspeech: Fast, robust and controllable text to speech

International Speech Communication Association

booktitle22nd Annual Conference of the International Speech Communication Association, INTERSPEECH 2021
titleAutomatically Detecting Errors and Disfluencies in Read Speech to Predict Cognitive Impairment in People with Parkinson’s Disease

titleBias and Statistical Significance in Evaluating Speech Synthesis with Mean Opinion Scores.

journaltitleProceedings of the IRE
titlePerceptron simulation experiments

journaltitlearXiv preprint arXiv:1911.04862
titleAn End-to-end Approach for Lexical Stress Detection based on Transformer

abstractThis paper describes the acquisition of a new database of dysarthric speech in terms of aligned acoustics and articulatory data. This database currently includes data from seven individuals with speech impediments caused by cerebral palsy or amyotrophic lateral sclerosis and age- and gender-matched control subjects. Each of the individuals with speech impediments are given standardized assessments of speech-motor function by a speech-language pathologist. Acoustic data is obtained by one head-mounted and one directional microphone. Articulatory data is obtained by electromagnetic articulography, which allows the measurement of the tongue and other articulators during speech, and by 3D reconstruction from binocular video sequences. The stimuli are obtained from a variety of sources including the TIMIT database, lists of identified phonetic contrasts, and assessments of speech intelligibility. This paper also includes some analysis as to how dysarthric speech differs from non-dysarthric speech according to features such as length of phonemes, and pronunciation errors.
journaltitleLanguage Resources and Evaluation
titleThe TORGO database of acoustic and articulatory speech from speakers with dysarthria

Cambridge University Press

titleBayesian filtering and smoothing

abstractIn this paper, automatic dysarthria severity classification is explored as a tool to advance objective intelligibility prediction of spastic dysarthric speech. A Mahalanobis distance-based discriminant analysis classifier is developed based on a set of acoustic features formerly proposed for intelligibility prediction and voice pathology assessment. Feature selection is used to sift salient features for both the disorder severity classification and intelligibility prediction tasks. Experimental results show that a two-level severity classifier combined with a 9-dimensional intelligibility prediction mapping can achieve 0.92 correlation and 12.52 root-mean-square error with subjective intelligibility ratings. The effects of classification errors on intelligibility accuracy are also explored and shown to be insignificant.
titleAutomated Dysarthria Severity Classification for Improved Objective Intelligibility Assessment of Spastic Dysarthric Speech.

journaltitleInternational Telecommunication Union Radiocommunication Assembly
titleMethod for the subjective assessment of intermediate quality level of audio systems

booktitleProc. 11th ISCA Speech Synthesis Workshop (SSW 11)
titleNon-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech

titleAutomatic Classification of Lexical Stress in English and Arabic Languages Using Deep Learning.

journaltitleJournal of Phonetics
titleStress shift and early pitch accent placement in lexical items in American English

titleTowards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron

booktitleinternational conference on machine learning
titleTowards end-to-end prosody transfer for expressive speech synthesis with tacotron

Wiley Online Library

journaltitleMethods in Ecology and Evolution
titleThe area under the precision-recall curve as a performance metric for rare binary events

journaltitleJournal of Machine Learning Research
titleDropout: A Simple Way to Prevent Neural Networks from Overfitting

titleMost common languages used on the internet as of January 2020, by share of internet users

titleNoise robust goodness of pronunciation measures using teacher's utterance

titleAn Improved Goodness of Pronunciation (GoP) Measure for Pronunciation Evaluation with DNN-HMM System Considering HMM Transition Probabilities.

booktitleAdvances in neural information processing systems
titleSequence to sequence learning with neural networks

journaltitleIEEE Transactions on Learning Technologies
titleAssessing pronunciation improvement in students of English using a controlled computer-assisted pronunciation tool

Cambridge University Press

titleA history of the mathematical theory of probability

journaltitleEnglish Phonetics and Phonology
titleThe production of speech sounds

booktitleInterspeech 2017, 18th Annual Conference of the International Speech Communication Association, Stockholm, Sweden, August 20-24, 2017
titleInterpretable Objective Assessment of Dysarthric Speech Based on Deep Neural Networks

UNESCO Paris

journaltitlePolicy Paper 24 of Global Education Monitoring Report
titleIf you don’t understand, how can you learn?

titleUniversity physics Volume 1

Multidisciplinary Digital Publishing Institute

titleDevelopment and Evaluation of Speech Synthesis System Based on Deep Learning Models

journaltitleAdvances in Neural Information Processing Systems
titleNeural discrete representation learning

booktitleInterspeech 2018, 19th Annual Conference of the International Speech Communication Association, Hyderabad, India, 2-6 September 2018.
titleA Multitask Learning Approach to Assess the Dysarthria Severity in Patients with Parkinson's Disease

titleAttention Is All You Need

booktitleAdvances in neural information processing systems
titleAttention is all you need

booktitleProceedings of the 10th Speech Synthesis Workshop (SSW10)
titleSpeech synthesis evaluation—state-of-the-art assessment and suggestion for a novel research program

titleChild Speech Disorder Detection with Siamese Recurrent Network Using Speech Attribute Features.

titleTacotron: {A} Fully End-to-End Text-To-Speech Synthesis Model

journaltitlearXiv preprint arXiv:1703.10135
titleTacotron: Towards end-to-end speech synthesis

booktitle24th IEEE SPA
titleConstructing a Dataset of Speech Recordings with Lombard Effect

journaltitleIEEE Information Theory Society Newsletter
titleHidden Markov models and the Baum-Welch algorithm

MIT press Cambridge, MA

titleGaussian processes for machine learning

journaltitleSpeech communication
titlePhone-level pronunciation scoring and assessment for interactive language learning

booktitle2016 international conference on digital image computing: techniques and applications (DICTA)
titleUnderstanding data augmentation for classification: when to warp?

Wiley Online Library

journaltitleWiley encyclopedia of clinical trials
titleWilcoxon signed-rank test

World Economic Forum

titleSpeaking more than one language can boost economic growth

titlePaired phone-posteriors approach to esl pronunciation quality assessment

booktitleProc. Interspeech 2021
titleExplore wav2vec 2.0 for Mispronunciation Detection

abstractIn this invited paper, we overview the clinical applications of speech synthesis technologies and explain a few selected researches. We also introduce the University of Edinburgh's new project “Voice Banking and reconstruction” for patients with degenerative diseases, such as motor neurone disease and Parkinson's disease and show how speech synthesis technologies can improve the quality of life for the patients.
journaltitleAcoustical Science and Technology
titleSpeech synthesis technologies for individuals with vocal disabilities: Voice banking and reconstruction

journaltitlearXiv preprint arXiv:2103.03023
titleEnd-to-End Mispronunciation Detection and Diagnosis From Raw Waveforms

journaltitlearXiv preprint arXiv:2108.13816
titleMaximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech

booktitleProc. Interspeech 2020
titleAn End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling

booktitleProc. Interspeech 2019
titleLibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech

booktitleaccepted to Interspeech 2022
titleL2-GEN: A Neural Phoneme Paraphrasing Approach to L2 Speech Synthesis for Mispronunciation Diagnosis

titleLearning latent representations for style control and transfer in end-to-end speech synthesis

Multidisciplinary Digital Publishing Institute

titleEnd-to-end automatic pronunciation error detection based on improved hybrid ctc/attention architecture

journaltitleSpeech Communication
titleText-conditioned Transformer for automatic pronunciation error detection

journaltitlePerception Sensing Instrumentation Lab
titleL2-ARCTIC: A non-native English speech corpus

journaltitleProc. APSIPA ASC
titleAutomatic lexical stress detection using acoustic features for computer assisted language learning