# Automatic Detection of Speech Sound Disorder in Child Speech Using
Posterior-based Speaker Representations

###### Abstract

This paper presents a macroscopic approach to automatic detection of speech
sound disorder (SSD) in child speech. Typically, SSD is manifested by
persistent articulation and phonological errors on specific phonemes in the
language. The disorder can be detected by focally analyzing the phonemes or
the words elicited by the child subject. In the present study, instead of
attempting to detect individual phone- and word-level errors, we propose to
extract a subject-level representation from a long utterance that is
constructed by concatenating multiple test words. The speaker verification
approach, and posterior features generated by deep neural network models, are
applied to derive various types of holistic representations. A linear
classifier is trained to differentiate disordered speech in normal one. On the
task of detecting SSD in Cantonese-speaking children, experimental results
show that the proposed approach achieves improved detection performance over
previous method that requires fusing phone-level detection results. Using
articulatory posterior features to derive i-vectors from multiple-word
utterances achieves an unweighted average recall of 78.2% and a macro F1 score
of 78.0%.

Index Terms: child speech, speech sound disorder, speaker representation,
articulatory feature, speech attributes.

##  1 Introduction

Speech sound disorder (SSD) is a common type of communication disorders
affecting 2.5% - 13.8% of children aged below 8 [1, 2, 3, 4]. When growing up,
children are expected to master the language's speech sounds in stages and be
able to self-correct the pronunciation mistakes. A significant portion of
children may encounter persistent difficulties in correctly producing certain
speech sounds after the expected stage of acquisition. The symptom is one
diagnostic attribute of SSD in clinical speech therapy. Untreated children
with SSD are prone to unsatisfactory social and educational outcomes [5, 6].
Early diagnosis and intervention are necessary for effective treatment and
rehabilitation. At present, clinical diagnosis of SSD is carried out by
qualified speech and language pathologists (SLP). The evaluation of the speech
sound inventories of children reveals the presence of SSD.

To timely identify children with SSD and refer to SLP for intervention,
automated detection of speech disorder is considered a highly desirable
approach for assessment and/or screening a large population of children.
Detection of SSD is commonly formulated as a task of distinguishing disordered
speech sounds from typical ones at phoneme-level, based on acoustic speech
signals. Siamese neural networks were adopted to contrast hypothetically
disordered consonant segments with typical ones [7, 8]. Posterior features
were derived from automatic speech recognition systems to facilitate
mispronunciation detection in disordered child speech [9, 10]. Considering
that consonant error could alter the acoustical characteristics of its
neighbouring vowel, our recent work proposed to detect consonant errors from
consonant-vowel speech segments [11]. The phoneme-level error detection
requires automatic time alignment of the target phoneme segments. Time
alignment could be inaccurate for disordered speech recorded in naturalistic
communication scenarios. Inaccurate segments would affect the efficacy of
model training and the detection performance.

Taking a different perspective, detection of SSD can also be formulated as a
problem of measuring the overall goodness of the child's speech production,
without going into specific parts of a long utterance. The reliability concern
of segment boundaries is thus bypassed. Paralinguistic features defined in the
extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS) represent the
overall acoustic characteristics of a speech utterance using statistical
functionals [12]. They were successfully applied to detect atypical speech,
including disordered speech with phonological and articulation problems [13].
The approach of speaker verification (SV) was also investigated in classifying
disordered speech [14, 15, 16, 17]. In [18, 19], SSD subjects were identified
by a text-dependent SV system trained on single-word utterances. The word-
level detection results were fused to give a subject-level score as an overall
assessment of word articulation.

Spectral features, e.g. Mel-frequency cepstrum coefficients (MFCCs), have been
routinely used to extract speaker representations. Posterior features derived
from deep neural network (DNN) classifiers are closely related to the
linguistic properties of speech [20, 21]. They particularly characterize the
articulation aspect of speech sounds. Compared to conventional spectral
features, posteriors are believed to convey more pertinent information that
can help detect atypical speech sounds [7, 10, 9]. We expect that using
speaker representations extracted from posterior features would effectively
differntiate disordered children from typically developing (TD) ones.

In the present study, we investigate the possibility of transforming the word
articulation test for SSD detection into a holistic assessment of multiple
word utterances. Prescribed target words are grouped together, and the
subject-level judgement is given based on the overall discrepancy between
disordered speech and typical speech. The detection does not require precisely
locating, analysing and classifying individual phones/words. Towards capturing
the coarse-grained speech characteristics of long utterances, speaker
representations such as i-vectors and x-vectors have been investigated [22,
23]. Using word-level utterances ensures that every single error in the
pronunciation can be covered, and speech duration would not be too short for
feature extraction [24]. In view of the limited amount of child speech data,
we need to determine the type of speaker representation that can perform well
in the low-resource scenario. We also investigate the use of knowledge-driven
posteriors in the extraction of speaker representations. Grouping the phonemes
based on speech attributes is adopted in the training of posterior extractors,
as speech attributes were shown to provide diagnostic information about SSD
symptoms [7, 10]. We will compare the proposed speaker representations for
subject-level SSD detection with phone-level and word-level approaches.

##  2 Child Speech Database

This research is focused on SSD in Cantonese-speaking pre-school children.
Cantonese is a major Chinese dialect widely spoken by millions of people in
Hong Kong, Macau, Guangdong Provinces of Mainland China, and many overseas
Chinese communities. It is a monosyllabic and tonal language. Each Chinese
character is pronounced as a single syllable carrying a lexical tone. There
are $19$ initial consonants, $11$ vowels, $10$ diphthongs, $6$ final
consonants and $6$ lexical tones [25, 26].

Table 1: Speech attributes of Cantonese.

Category | Feature | Phoneme
---|---|---
| Plosive |  p ph t th k kh kw kwh
| Nasal | m n N
| Affricate |  ts tsh
| Fricative | s f h
| Glide | j w
Manner | Liquid | l
| Aspirated |  ph th kh kwh tsh
Aspiration | Unaspirated |  p t k kw ts
| Alveolar |  t th ts tsh s j
| Lateral | l
| Labial |  p ph w m
| Velar |  k kh N
| Labio-Velar |  kw kwh
| Labio-dental | f
Place | Vocal | h
Vowel/Semi-vowel |  | a: i: E: e œ: œ O: o u: y: 5 I 8 U

Experiments on subject-level SSD detection are carried out with a large-scale
child speech database named CUCHILD [27]. The database contains speech data
from $1,986$ kindergarten children aged $3$ \- $6$ in Hong Kong. All speakers
use Cantonese as their first language. The speech materials consist of $130$
Cantonese words of $1$ to $4$ syllables in length. These words cover all
Cantonese consonants and vowels. Each subject in CUCHILD was formally assessed
with the Hong Kong Cantonese Articulation Test (HKCAT) [28]. About $230$
children in the database were found to have SSD. Most of these children
committed speech sound errors more frequently in the initials consonants than
vowels.

We select speech data from $415$ subjects in CUCHILD, including $265$
typically developing (TD) children and $150$ disordered. 48% of the TD and 61%
of the disordered children are aged 3-4. Speech sound errors made by the
disordered speakers are carefully annotated by four student clinicians from
speech therapy programmes. Given the heterogeneous acoustic environment of
speech recording, for some subjects, a few spoken word items could not be
located or were contaminated by background noise. $125\pm 14$ word utterances
are available for each speaker. For each disordered speakers, $45\pm 25$ word
utterances are annotated as having speech sound errors. 5-fold cross-
validation experiments are carried out. 80% of the TD and disordered speakers
are used in model training, i.e. about $5.5$ hours of training data in each
fold. Speech data from the training speakers does not appear in the test set.

##  3 Proposed System and Feature Design

###  3.1 System Architecture

In the standardized assessment of SSD for young children, a set of designated
test words are used for all subjects. The test words are selected purposely
based on linguistic and clinical knowledge to cover the target speech sounds
(phonemes) to be evaluated [28, 29]. Our proposed system for subject-level SSD
detection is shown as in Figure 1. For each child subject, speech segments
that contain the test words are concatenated to construct a long utterance of
multiple words. Front-end feature processing involves the computation of
filter-bank features, MFCCs or eGeMAPS. I-vector or x-vector is derived from
the MFCCs as a speaker-level feature representation. The i-vector can also be
obtained from the posterior features generated by a DNN. A support vector
machine (SVM) is trained on the i-vector, x-vector or eGeMAPS to determine if
the subject is TD or disordered.

Figure 1: Subject-level SSD detection system.

###  3.2 I-vector and x-vector

I-vector is a fixed-dimensional representation of variable-length utterance
from a speaker. Consider an utterance $i$. It is modeled by a super-vector,
defined as $\bm{\mu}_{i}=\bm{m}+\bm{T}\bm{w}_{i}$, where $\bm{m}$ is the mean
vector obtained from Gaussian Mixture Model - Universal Background Model (GMM-
UBM). The GMM-UBM is the speaker-independent component trained on all
speakers. $\bm{T}$ and $\bm{w_{i}}$ are the speaker-dependent components.
$\bm{T}$ is the total variability matrix and $\bm{w_{i}}$ is the latent
factor, known as the i-vector. Training of i-vector extractor is implemented
by the expectation maximization (EM) algorithm.

x-vector is extracted from a DNN model [23]. A time-delay neural network
(TDNN) architecture is adopted and trained with a cross-entropy objective
function. Training of the TDNN requires splitting an utterance into segments.
x-vectors of the segments are computed by aggregating the hidden
representations of the TDNN via mean and variance pooling. They are averaged
to derive the subject-level x-vectors.

I-vector and x-vector were applied to detecting speech-related diseases, e.g.
dysarthria [14], Parkinson's disease [15], and oral cancer [16, 17]. It was
common to train the extractors using out-of-domain corpus, e.g. from healthy
adult speakers. In the present study, apart from using out-of-domain data, we
attempt to use in-domain child speech data, which incorporates disordered
speech in training.

###  3.3 Posterior feature for i-vector extraction

Consonant substitution is one of the major types of speech sound errors in
child speech. The errors are usually described as specific categories of
substitution patterns. For example, the Cantonese initial consonant /t/ being
substituted by /k/ is known as the ``backing'' error. /tsh/ substituted by /s/
is described as a ``de-affrication'' error. These errors correspond to the
change of speech attributes, i.e., the manner and place of articulation,
and/or the aspiration status. In the example of /t/→/k/, the place of
articulation shifts from bilabial to alveolar, while the example of /tsh/→/s/
is due to change in the manner of articulation from affricate to fricative.
The grouping of phonemes based on speech attributes is listed in Table 1. The
Cantonese phonemes are represented in the international phonetic alphabet
(IPA) symbols.

Posterior features derived from a DNN based speech attribute/phone classifier
provide information related to the contrast and similarity between phonemes.
The classifier adopts the architecture of bi-directional gated recurrent unit
(Bi-GRU). The inputs to the Bi-GRUs are filter-bank features. Posteriors are
extracted from the Bi-GRU output at each time frame using a fully connected
layer. The posteriors are transformed into log-posterior ratio (LPR), which is
defined as

$\begin{split}\text{LPR}_{i}=\log(\dfrac{p_{i}}{1-p_{i}}),\end{split}$ (1)

where $i=1,2,...,N$, with $N$ being the total number of phone/articulatory
classes. $p_{i}$ represents the posterior probability of the $i$-th class
[30]. Subsequently, the LPRs are used for the i-vector modeling.

###  3.4 Paralinguistic features

The eGeMAPS is a minimal set of paralinguistic parameters measured on speech
signals [12]. A collection of low-level descriptors (LLDs) are computed to
characterize the speech signal's frequency, energy, and spectral aspects at
frame-level. The LLDs include acoustic measures such as pitch, formant
frequency, MFCC, shimmer, jitter and spectral energy, etc. An 88-dimensional
feature vector is derived by applying statistical functionals to the LLDs.
Extraction of eGeMAPS features is implemented by the OpenSmile toolkit [31].

##  4 Fusing Phone/Word-level Results

Conventionally, SLPs observe the child's speech sound errors in specific parts
of the test words. The results are recapitulated for diagnosis of the child.
Our proposed approach based on speaker-level representation is compared with
methods in which word-level or phone-level error detection is done first on
individual test words and subsequently combined to obtain the subject-level
decision [11, 13, 18].

Phone-level detection is implemented based on our previous work in [11]. A DNN
model is trained on the classification task to extract fixed-dimension
embeddings of consonant-vowel segments. For detection, each test embedding
$\bm{x_{test}}$ is compared with the reference embeddings $\bm{x_{ref}}$ from
TD speakers in a pairwise manner. Pairs of embeddings from TD speech are
generated to train a logistic regression (LR) classifier based on the
similarities given by $||\bm{x_{test}}-\bm{x_{ref}||_{1}}$. The classification
output tells whether the consonant-vowel segments in the test-reference pair
are different. For each speaker, the accuracy score of detecting each initial
consonant is stacked to derive a speaker-level representation that describes
the child's performance on the articulation test. A SVM is trained on the
representations to classify the test subject as TD or disordered.

For word-level detection, i-vector, x-vector, or eGeMAPS can be extracted from
single-word utterances. These representations are similar to the speaker-level
representations introduced in Section 3, except that one representation would
be obtained from each test word. Training labels of word-level representations
are inherited from the speaker-level diagnostic labels, i.e. TD or SSD. An LR
classifier is trained on the word-level representations. The majority of word-
level classification results determines the subject-level result.

##  5 Experimental Set-up

###  5.1 I-vector and x-vector extraction

The i-vector and x-vector systems are implemented with the Kaldi toolkit [32].
Four i-vector systems are evaluated in our experiments. Training of the first
system follows the approach in [18] using MFCCs and single-word utterances.
The other three systems are trained on long utterances of multiple words for
extracting speaker-level representation. The input features are MFCCs, speech
attribute LPR and phone LPR, respectively. The i-vector systems use 256
mixture components, and the dimension of i-vectors is 100.

Three different x-vector systems are trained and compared with the i-vector
systems. The network architecture in [23] is used with that the size of hidden
layers reduced by half. The generated x-vector has a dimension of $256$. The
first x-vector system is built using an out-of-domain corpus called CUSENT,
which contains about $20$ hours of Cantonese speech from $76$ adults speakers
[33]. The network is trained with speaker labels. The other two x-vector
systems are trained with child speech, using long utterances of multiple
words, and single-word utterances, respectively. When single-word utterances
are used, discriminative training of x-vector extractors is carried out with
the speaker labels. When utterances of multiple words are used, diagnostic
labels (SSD or TD) are used to capture the difference between different TD and
disordered speakers.

###  5.2 Posterior and embedding extractors

The phone-level or speech-attribute posterior extractors and the consonant-
vowel embedding extractors all use the same neural network model with 3 layers
of Bi-GRUs. The input acoustic features are 80-dimensional filter-bank
features extracted every 0.01 second, and mean and variance normalised.
Training speech data consist of consonant-vowels segments extracted obtained
by forced alignment. The embedding and posterior extractors are trained as a
softmax-based multi-class classification task with cross-entropy objective
functions. The number of classification targets for phone and consonant-vowel
units is $33$ and $173$, respectively. Multi-task training is carried out on
16 speech attributes for speech attribute posterior extractors. The Adam
optimizer is applied with a learning rate of $0.0001$ for the posterior
extractors and $0.001$ for the embedding extractors [34]. The posterior
extractors are built using the Phonet toolkit [35] toolkit. The consonant-
vowel embedding extractors are implemented by PyTorch [36].

###  5.3 Back-end classifier for SSD detection

SVM and LR classifiers for phone-, word- and subject-level detection are
trained on speaker-level representations or paralinguistic features using
sklearn [37]. We assign positive label `1' to the representations of
disordered speakers and negative label `0' to the TD ones. The SVM uses a
linear kernel. The regularization parameter of SVM and LR is $1.0$.

Table 2: Classification performance of embedding and posterior extractors.

Extractor | Metric | Performance
---|---|---
Consonant-vowel | Accuracy | 88.0 (0.9)
Phone | 70.8 (1.7)
Speech attribute | Max. UAR (Vocal) | 95.4 (1.0)
Min. UAR (Velar) | 83.1 (0.6)

Table 3: Performance of subject-level SSD detection using the proposed
approach.

Method |  Dim. Reduction | Classifier | UAR | Macro F1
---|---|---|---|---
Subject
eGeMAPs | - | SVM | 65.1 (5.3) | 63.9 (5.1)
x-vector | 65.7 (6.2) | 65.2 (6.1)
|  x-vector(CUSENT)
---
68.2 (5.9) | 67.1 (7.0)
i-vector(CUSENT) | - | SVM | 69.9 (3.6) | 68.6 (4.0)
LDA | 72.0 (5.8) | 71.3 (5.3)
i-vector | - | 71.7 (6.3) | 71.7 (6.3)
LDA | 72.5 (7.3) | 72.4 (7.7)
i-vector (phone LPR) | - | 74.5 (7.9) | 73.8 (6.5)
LDA | 76.2 (6.5) | 76.0 (6.6)
i-vector (speech attribute LPR) | - | 76.9 (5.3) | 76.5 (5.6)
LDA | 78.2 (4.6) | 78.0 (4.9)

##  6 Results and Discussion

The classification performance of the embedding and posterior extractors is
evaluated on the speech data from TD speakers in each cross-validation fold.
Results are reported in Table 2 in terms of classification accuracy and
unweighted average recall (UAR). Overall, the extractors produce a fairly
satisfactory performance in classifying the consonant-vowels, phones and
speech attributes. The embeddings or posterior features extracted from child
speech utterances are deemed applicable and effective to subject-level SSD
detection.

The 5-fold cross-validation results of subject-level SSD detection using the
proposed approach are given in Table 3 in terms of UAR and the macro F1 score.
With long utterances of multiple words, the i-vector approach surpasses
x-vector and eGeMAPS. The i-vector approach remains competitive in the low-
resource scenario. Training i-vector and x-vector extractors on out-of-domain
adult speech data lead to comparable performance, where i-vector slightly
outperforms x-vector. In-domain child speech is preferable in the training of
the i-vector system. Using eGeMAPS is less competitive in the detection. Given
SSD is mainly manifested in phonological errors, the paralinguistic
information captured by eGeMAPS may be less informative about the contrast
between TD and disordered speech.

Using speech attribute LPR in the i-vector extraction achieves the best
detection performance, followed by phone LPR. The speech attribute LPR
describes multiple changes in articulation and aspiration status. It contains
richer diagnostic information than the phone LPR. Reducing the dimensionality
of i-vector with linear discriminant analysis (LDA) further improves the
detection and reduces the mis-classification of disordered speakers, giving a
UAR of $78.2$% and a macro F1 score of $78.0$%.

The results of the best performed i-vector system are analysed using a two-
tailed z-test. A significant threshold of 0.05 is assumed. The mis-classified
subjects are found to be significantly younger than correctly classified
subjects. Given word-level ground-truth annotations, the average number of
errors in the false negative subjects is significantly less than in the true
positive subjects. From the clinical perspective, TD children of younger age,
i.e. about 3-4, are allowed to make more mistakes than older children in the
articulation test. The development of their phonemic inventories and oral
motor skills has just begun. The ambiguity caused by erroneous pronunciations
in young TD and disordered subjects makes SSD detection more challenging.

Table 4: Performance of subject-level SSD detection based on aggregating
word-/phone-level detection results.

Method | Classifier | UAR | Macro F1
---|---|---|---
Segment | Subject
eGeMAPs | LR | Majority | 59.4 (8.0) | 58.5 (9.5)
x-vector | 63.7 (5.6) | 63.7 (6.0)
i-vector | 65.6 (1.9) | 65.9 (2.3)
| Consonant-vowel
---
embedding
LR | SVM | 75.6 (4.3) | 72.6 (3.9)

As a comparison to the proposed detection approach, results of subject-level
SSD detection based on aggregating phone-level and word-level detection
results are given in Table 4. Aggregating phone-level detection results from
consonant-vowel embeddings deliver competitive performance. The detection
performance with a UAR of $75.6$% and a macro F1 score of $72.6$% is the best
among detection methods based on word-level and phone-level results. The
performance is also on par with the i-vector approach using MFCCs and
utterances of multiple words. Information about context-dependency in
consonant errors is effective for detection.

For SSD detection based on word-level results, the i-vector approach delivers
a satisfactory performance with $65.6$% in UAR and $65.9$% in macro F1 score.
$62\pm 18$% of the word utterances are classified as correct pronunciations in
the disordered speakers. $18.4\pm 14.6$% of the word utterances are classified
as erroneous in TD speakers. The results echo that disordered children can
correctly produce a significant portion of the test words. TD children are
also allowed to make age-appropriate errors. In the training of classifiers
for word-level SSD detection, it would be sub-optimal to assume all word
utterances from a speaker are errorless or related to SSD symptoms.

##  7 Conclusion

In this study, we demonstrate the use of knowledge-driven posterior-based
features in subject-level SSD detection based on speaker representations. It
has been shown that using fixed-dimensional speaker representation extracted
from an aggregation of test words is a feasible and effective approach to
detecting SSD in child speech. The proposed approach surpasses traditional
methods that combine fine-grained classification results on individual target
phones/words. In the low-resource scenario, the i-vector approach outperforms
the x-vector one and paralinguistic features. For disorder symptoms that are
mainly manifested in phonological errors, extraction of i-vector using
posterior-based features can improve detection performance over conventional
acoustic features. Our future work will focus on using self-supervised
representation learning and neural confidence measure for subject-level SSD
detection.

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