# Advancing African-Accented English Speech Recognition: Epistemic
Uncertainty-Driven Data Selection for Generalizable ASR Models

Bonaventure F. P. Dossou
McGill University, Mila Quebec AI Institute
<EMAIL_ADDRESS>
###### Abstract

Accents play a pivotal role in shaping human communication, enhancing our
ability to convey and comprehend messages with clarity and cultural nuance.
While there has been significant progress in Automatic Speech Recognition
(ASR), African-accented English ASR has been understudied due to a lack of
training datasets, which are often expensive to create and demand colossal
human labor. Combining several active learning paradigms and the core-set
approach, we propose a new multi-rounds adaptation process that uses epistemic
uncertainty to automate the annotation process, significantly reducing the
associated costs and human labor. This novel method streamlines data
annotation and strategically selects data samples that contribute most to
model uncertainty, thereby enhancing training efficiency. We define a new
metric called U-WER to track model adaptation to hard accents. We evaluate our
approach across several domains, datasets, and high-performing speech models.
Our results show that our approach leads to a 69.44% WER improvement while
requiring on average 45% less data than established baselines. Our approach
also improves out-of-distribution generalization for very low-resource
accents, demonstrating its viability for building generalizable ASR models in
the context of accented African ASR. We open-source the code here.

##  1 Introduction

Automatic Speech Recognition (ASR) is an active research area that powers
voice assistant systems (VASs) like Siri and Cortana, enhancing daily
communication [27, 17, 46]. Despite this progress, no current VASs include
African languages, which account for about 31% of the world languages, and
their unique accents [16, 44]. This gap underscores the need for ASR systems
that can handle the linguistic diversity and complexity of African languages,
especially in crucial applications like healthcare. Due to the lack of
representation of these languages and accents in training data, existing ASR
systems often perform inadequately, even mispronouncing African names [36].

To address these challenges, our work focuses on adapting pretrained speech
models to better transcribe African-accented English, defined by unique
intonations and pronunciations [7, 22]. We use epistemic uncertainty (EU) [25]
to guide the adaptation process by identifying gaps in model knowledge and
prioritizing data for the model to learn from next. This is particularly
beneficial in scenarios where data annotation is costly or time-consuming, as
often seen in the African context [5, 4, 6, 45, 12, 14, 13]. EU also improves
robustness and encourages exploration to mitigate inductive bias from
underrepresented accents. Common approaches to compute EU include Monte Carlo
Dropout (Mc-Dropout) [18] and Deep Ensembles [30], with the latter being more
effective but computationally expensive. Due to resource constraints, we use
Mc-Dropout, which requires models to have dropout components during
pretraining.

To further enhance the efficiency and effectiveness of model adaptation, we
employ Active Learning (AL) techniques. AL leverages epistemic uncertainty to
select the most informative data points from an unlabeled dataset for
labeling, thereby improving model performance with fewer training instances.
Common types of AL include Deep Bayesian Active Learning (DBAL) [19, 23] and
Adversarial Active Learning (AAL) [15]. AAL selects examples likely to be
misclassified by the current model, refining it iteratively by challenging it
with difficult cases to enhance robustness. The core-set approach (CSA) [42]
is also related, as it selects a subset of training data to ensure a model
trained on this subset performs comparably to one trained on the full dataset,
addressing scalability and efficiency. A critical component of AL is the
acquisition function (AF), which determines the most informative samples from
an unlabeled dataset for labeling. Key AFs include uncertainty sampling (US)
[31], Bayesian Active Learning by Disagreement (BALD) [19], and BatchBALD
[26]. US targets data points with the highest model uncertainty. BALD
maximizes the mutual information between model parameters and predictions.
BatchBALD is an extension of BALD that selects multiple samples simultaneously
but may choose redundant points. US is the least computationally expensive,
making it ideal for efficient data labeling.

In this work, we leverage and combine DBAL, AAL, US, and CSA in the following
way (in order): First, we integrate the CSA by leveraging smaller training
subsets ($\sim 45\%$ smaller than the full available training sets). Second,
we use DBAL with MC-Dropout, to apply dropout during training and inference to
estimate Bayesian posterior distribution. This allows us to practically and
efficiently estimate EU in the models used [19] (see section 3.2 for more
details). Third, we use the estimated EU and integrate the idea of AAL by
using the US acquisition function.

We evaluate our approach across several domains (general, clinical,
general+clinical aka both), several datasets (AfriSpeech-200 [37], SautiDB
[2], MedicalSpeech, CommonVoices English Accented Dataset [3]), and several
high-performing speech models (Wav2Vec2-XLSR-53 [10], HuBERT-Large [24],
WavLM-Large [8], and NVIDIA Conformer-CTC Large (en-US) [20]). Our results
show a 69.44% WER improvement while requiring on average 45% less data than
established baselines. We also adapt the standard Word Error Rate (WER) to
create a new metric called Uncertainty WER (U-WER) to track model adaptation
to African accents.

The impact of our approach is substantial. It develops more robust,
generalizable, and cost-efficient African-accented English ASR models and
reduces dependency on large labeled datasets, enabling deployment in various
real-world scenarios. Our results show improved generalization for out-of-
distribution (OOD) cases, especially for accents with minimal resources,
addressing specific challenges in African-accented ASR. Additionally, by
focusing on equitable representation in ASR training, our methodology promotes
fairness in AI, ensuring technology serves users across diverse linguistic
backgrounds without bias [41, 34, 32]. Our contributions are listed as
follows:

  * •

we combine DBAL, AAL, CSA, and EU to propose a novel way to adapt several
high-performing pretrained speech models to build efficient African-accented
English ASR models,

  * •

we evaluate our approach across several speech domains (clinical, general,
both), and African-accented speech datasets (AfriSpeech-200 [37], SautiDB [2],
MedicalSpeech and CommonVoices English Accented Dataset [3]), while providing
domain and accent-specific analyses,

  * •

we define a new and simple metric called U-WER that allows us to measure and
track how the variance of the model, across hard accents, changes over the
adaptation process,

  * •

we show that our approach improves the WER performance by 69.44% while
significantly reducing the required amount of labeled data (by $\sim$45

  * •

we show, based on additional AL experiments, that our approach is also
efficient in real-world settings where there are no gold transcriptions.

##  2 Background and Related Works

###  2.1 Challenges for African-accented ASR

State-of-the-art (SOTA) ASR technologies, powered by deep learning and neural
network architectures like transformers, achieve high accuracy with Standard
American English and major European languages. However, they often fail with
African accents due to high variability in pronunciation and lack of quality
speech data [29, 11]. This results in racial bias, poor performance, and
potential social exclusion as speakers might alter their speech to be
understood [29, 28, 9, 33]. Enhancing ASR for African languages is crucial for
equitable voice recognition, especially in healthcare, education, and customer
service. Solutions should focus on diversifying training datasets and
developing robust modeling techniques tailored to these languages unique
characteristics.

###  2.2 Active Learning

AL aims to reduce the number of labeled training examples by automatically
processing the unlabeled examples and then selecting the most informative ones
concerning a given cost function for a human to label. It is particularly
effective when labeled data is scarce or expensive, optimizing the learning
process by focusing on samples that most improve the model performance and
generalization [43, 19]. Several works have demonstrated its effectiveness and
efficiency. An AL setup involves an unlabeled dataset
$\mathcal{D}_{\mathrm{pool}}=\\{\mathbf{x}_{i}\\}_{i=1}^{n_{\mathrm{pool}}}$,
a labeled training set
$\mathcal{D}_{\mathrm{train}}=\\{\mathbf{x}_{i},\mathrm{y}_{i}\\}_{i=1}^{n_{\mathrm{train}}}$,
and a predictive model with likelihood $p_{w}(y|x)$ parameterized by $w\sim
p(W|\mathcal{D}_{\mathrm{train}})$. The setup assumes the presence of an
oracle to provide predictions $\mathrm{y}$ for all
$x_{i}\in\mathcal{D}_{\mathrm{pool}}$. After training, a batch of data
$\\{\mathbf{x}^{*}_{i}\\}_{i=1}^{b}$ is selected from
$\mathcal{D}_{\mathrm{pool}}$ based on its EU.

In [21], AL was applied to a toy dataset of How May I Help You recordings.
Confidence scores were estimated for each word and used to compute the overall
confidence score for the audio sample. This approach achieved competitive
results using 27% less data compared to the baseline. In [40], the authors
estimated confidence scores for each utterance using an online algorithm with
the lattice output of a speech recognizer. The utterance scores were filtered
through an informativeness function to select an optimal subset of training
samples, reducing the labeled data needed for a given WER by over 60%. [35]
experimented with AL for accent adaptation in speech recognition. They adapted
a source recognizer to the target accent by selecting a small, matched subset
of utterances from a large, untranscribed, multi-accented corpus for human
transcription. They used a cross-entropy-based relevance measure alongside
uncertainty-based sampling. However, their experiments on Arabic and English
accents showed worse performance compared to baselines, while using more hours
of recordings.

Figure 1: Our adaptation pipeline involves several phases. Initially, the
dataset is split into a training set ($D1=\mathcal{D}_{\mathrm{train}}^{*}$,
30%) and a pool dataset ($D2=\mathcal{D}_{\mathrm{pool}}$, 70%). In the
iterative process between phases 2 and 3, $D1$ is used to finetune a
pretrained model. The top-k samples are selected using defined strategies and
added to $D1$ for the next round. For more details on the uncertainty
selection strategy, see section 3.2.

##  3 Datasets and Methodology

###  3.1 Datasets

We used the AfriSpeech-200 dataset [37], a 200-hour African-accented English
speech corpus for clinical and general ASR. This dataset includes 120+ African
accents from five language families: Afro-Asiatic, Indo-European, Khoe-Kwadi
(Hainum), Niger-Congo, and Nilo-Saharan, representing regional diversity
across Africa. It was crowd-sourced from over 2000 African speakers from 13
anglophone countries in sub-Saharan Africa and the US.

To demonstrate the dataset-agnostic nature of our approach, we also explored
three additional datasets: (1) SautiDB [1], Nigerian accent recordings with
919 audio samples at a 48kHz sampling rate, totaling 59 minutes; (2)
MedicalSpeech111https://www.kaggle.com/datasets/paultimothymooney/medical-
speech-transcription-and-intent, containing 6,661 audio utterances of common
medical symptoms, totaling 8 hours; and (3) CommonVoices English Accented
Dataset, a subset of English Common Voice (version 10) [3], excluding western
accents to focus on low-resource settings.

Table 1: AfriSpeech-200 Dataset statistics AfriSpeech Dataset Statistics
---
Total duration | 200.91 hrs
Total clips | 67,577
Unique Speakers | 2,463
Average Audio duration | 10.7 seconds
Speaker Gender Ratios - # Clip %
Female | 57.11%
Male | 42.41%
Other/Unknown | 0.48%
Speaker Age Groups - # Clips
<18yrs | 1,264 (1.88%)
19-25 | 36,728 (54.58%)
26-40 | 18,366 (27.29%)
41-55 | 10,374 (15.42%)
>56yrs | 563 (0.84%)
Clip Domain - # Clips
Clinical | 41,765 (61.80%)
General | 25,812 (38.20%)
Table 2: Dataset splits showing speakers, number of clips, and speech duration in Train/Dev/Test splits. AfriSpeech-200 Dataset Splits |
---|---
Item | Train ($\mathcal{D}_{\mathrm{train}}^{*}$) | Dev | Test | AL Top-k
# Speakers | 1466 | 247 | 750 | ✗
# Hours | 173.4 | 8.74 | 18.77 | ✗
# Accents | 71 | 45 | 108 | ✗
Avg secs/speaker | 425.81 | 127.32 | 90.08 | ✗
clips/speaker | 39.56 | 13.08 | 8.46 | ✗
speakers/accent | 20.65 | 5.49 | 6.94 | ✗
secs/accent | 8791.96 | 698.82 | 625.55 | ✗
# general domain | 21682 (*6504) | 1407 | 2723 | 2000
# clinical domain | 36318 (*10895) | 1824 | 3623 | 3500
# both domain | 58000 (*17400) | 3221 | 6346 | 6500

###  3.2 Methodology

Algorithm 1 Adaptation Round using Epistemic Uncertainty-based Selection

1:Pretrained Model $\mathcal{M}$, Training Dataset
$\mathcal{D}_{\mathrm{train}}^{*}$, Validation Dataset $\mathcal{D}_{Val}$,
and Pool Dataset $\mathcal{D}_{\mathrm{pool}}$

2:$\mathcal{N}\leftarrow 3$$\triangleright$ Number of Adaptation Rounds

3:$T\leftarrow 10$$\triangleright$ Number of Stochastic Forward Passes

4:for $k\leftarrow 1$ to $\mathcal{N}$ do

5: $g\leftarrow$ Finetune $\mathcal{M}$ on $\mathcal{D}_{\mathrm{train}}^{*}$
using $\mathcal{D}_{Val}$

6: $\mathcal{EUL}\leftarrow\\{\\}$ $\triangleright$ List of Uncertainty Scores

7: for $x$ in $\mathcal{D}_{\mathrm{pool}}$ do $\triangleright$ x is an audio
sample

8: EUx $\leftarrow$ EU($x|g,T$) $\triangleright$ Epistemic Uncertainty of $x$

9: $\mathcal{EUL}\leftarrow\mathcal{EUL}\cup\\{(x,\text{EU}_{x})\\}$

10: end for

11: $topk\leftarrow\\{x_{1},...,x_{k}\\}$ $\triangleright$ Samples with
highest $\mathcal{EU}$

12:
$\mathcal{D}_{\mathrm{train}}^{*}\leftarrow\mathcal{D}_{\mathrm{train}}^{*}\cup
topk$

13: $\mathcal{D}_{\mathrm{pool}}\leftarrow\mathcal{D}_{\mathrm{pool}}\setminus
topk$

14:end for

In our approach, to compute EU for a given input
$x\in\mathcal{D}_{\mathrm{pool}}$, we perform Mc-Dropout to obtain multiple
stochastic forward passes through a finetuned ASR model $g$ with likelihood
$p_{w\sim p(\mathbf{W}\mid\mathcal{D}_{\mathrm{train}}^{*})}(y|x)$ where
$\mathbf{W}$ is the weights of $g$. Let $f$ be a function that computes the
WER between the predicted and the target transcripts. Let $T$ be the number of
stochastic forward passes. For each pass $t$, we apply dropout, obtain the
output transcript, and compute the WER:

$f_{t}=f(y,\hat{y}_{t});\hat{y}_{t}=g(\mathbf{W},\tilde{x}_{t});\tilde{x}_{t}=x\cdot\mathbf{M}_{t}$

where $\mathbf{M}_{t}$ is a binary mask matrix sampled independently for each
pass. EU(($x|g,T$)) can then be estimated from the $T$ stochastic forward
passes as follows:

$\text{EU($x|g,T$)}=\sigma(f)=\sqrt{\frac{1}{T}\sum_{t=1}^{T}f_{t}^{2}-\left(\frac{1}{T}\sum_{t=1}^{T}f_{t}\right)^{2}}$
(1)

The use of Mc-Dropout requires models to have dropout components during
training. This excludes some models like Whisper [38], which we still
finetuned and evaluated as a baseline. We use four state-of-the-art pretrained
models: Wav2Vec2-XLSR-53, HuBERT-Large, WavLM-Large, and NVIDIA Conformer-CTC
Large (en-US), referred to as Wav2Vec, Hubert, WavLM, and Nemo, respectively.

####  3.2.1 Uncertainty WER

To handle diverse accents, we aim to reduce the EU of the models across hard
accents after each adaptation round. We define a metric called U-WER to track
this. To compute U-WER($a$) where $a$ is a hard accent, we condition EU on
$a$:

$\text{EU($x|g,T,{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}$)}=\sigma(f_{{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}})=\sqrt{\frac{1}{T}\sum_{t=1}^{T}f_{t,{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}}^{2}-\left(\frac{1}{T}\sum_{t=1}^{T}f_{t,{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}}\right)^{2}}$
(2)

where
$x_{{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}}$
is the audio sample with accent a and

$f_{t,{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}}=f(y_{{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}},\hat{y}_{t,{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}});\hat{y}_{t,{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}}=g(\mathbf{W},\tilde{x}_{t,{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}});\tilde{x}_{t,{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}}=x_{{\color[rgb]{0.8359375,0.3125,0.078125}\definecolor[named]{pgfstrokecolor}{rgb}{0.8359375,0.3125,0.078125}a}}\cdot\mathbf{M}_{t}$

Ideally, U-WER$\rightarrow$0\. The rationale behind U-WER is that as
beneficial data points are acquired, U-WER should decrease or remain constant,
indicating increased robustness, knowledge, and performance, crucial for
generalization. During AL, U-WER is computed using pairwise WER scores among
predicted transcriptions, not gold transcriptions (see section 3.3). To select
the best-generated transcript for unlabeled speech $x$, we follow Algorithm 2.

Algorithm 2 Selection of the best-generated transcript in Active Learning for
an input Sample $x$

1:we generate the predictions $\hat{y}_{1},..,\hat{y}_{T}$ corresponding to
each stochastic forward pass ($T$=10 in our experiments)

2:we define a list variable called wer_list and a dictionary variable called
wer_target_dict respectively tracking all pairwise WERs and the average
pairwise WER of each target prediction

3:for $\forall$ i,j $\in\\{1,...,T\\}$ do

4: $\rightarrow$ $\hat{y}_{i}$ is set as target transcription

5: $\rightarrow$ target_wer = list()

6: for for j $\neq$ i do

7: $w=WER(\hat{y}_{j},\hat{y}_{i})$

8: wer_list.append($w$)

9: target_wer.append($w$)

10: end for

11: $wer_{\hat{y}_{i}}$ = mean(target_wer)

12: wer_target_dict[$\hat{y}_{i}$] $\leftarrow$ $wer_{\hat{y}_{i}}$

13:end for

14:$\hat{y}_{best}$ = $\hat{y}_{i}$, such that wer_target_dict[$\hat{y}_{i}$]
= min(wer_target_dict.values())

15:return ($p_{best}$, std(wer_list))

###  3.3 Experimental Design

To work within our framework, we define the following selection strategies:

  * •

random: Randomly selects audio samples from $\mathcal{D}_{\mathrm{pool}}$.

  * •

EU-Most: Selects the most uncertain audio samples from
$\mathcal{D}_{\mathrm{pool}}$ to add to $\mathcal{D}_{\mathrm{train}}$.

  * •

AL-EU-Most: Combines AL with the EU-Most strategy to finetune the pretrained
model.

We also define standard fine-tuning (SFT) as baseline using all available data
for finetuning. In SFT, $\mathcal{D}_{\mathrm{pool}}$ is empty. While running
the defined strategies in our framework, we impose data constraints, not
exceeding 60-65% of the initial dataset after all adaptation rounds.
$\mathcal{D}_{\mathrm{train}}^{*}$ is 30% of $\mathcal{D}_{\mathrm{train}}$,
and $\mathcal{D}_{\mathrm{pool}}$ is 70% of $\mathcal{D}_{\mathrm{train}}$.
This simulates realistic scenarios where not all data might be available,
testing the approach’s robustness and efficiency under constraints. The number
of samples in $\mathcal{D}_{\mathrm{train}}$ and $\mathcal{D}_{\mathrm{pool}}$
is based on available training examples for each domain (see Tables 2, 4, and
Appendix A.1).

Our EU-based pipeline is shown in Figure 1 and Algorithm 1. In each adaptation
round, we use a finetuned model and a selection strategy to choose samples
from $\mathcal{D}_{\mathrm{pool}}$ to add to
$\mathcal{D}_{\mathrm{train}}^{*}$. During AL experiments, we consider samples
from $\mathcal{D}_{\mathrm{pool}}$ as unlabeled: (1) using Mc-Dropout, we
obtain $n=10$ different input representations per audio sample to get $n$
different transcripts; (2) we then learn to select the best-generated
transcription as the target transcription according to Algorithm 2.

Our experiments aim to answer the following research questions:

  1. 1.

how does the pretrained ASR model adapt to a set of African accents across
adaptation rounds and domains?

  2. 2.

which selection strategy (EU-most or random) works better, and for which
domain(s)?

  3. 3.

which domain(s) help the model perform better, and how does the model perform
(in terms of uncertainty) across the domain(s)?

  4. 4.

what is the impact of EU-based selection on the model’s efficiency in low-
resource data scenarios?

  5. 5.

is uncertainty-based selection, model, and dataset agnostic?

U-WER will answer question 4. To answer question 5, we evaluated our approach
with three additional pretrained models (Nemo, WavLM, and Hubert) and across
three external datasets (SautiDB, CommonVoices English Accented Dataset, and
MedicalSpeech). For consistency and better visualization, we considered the
top-10 (in terms of frequency) accents across three adaptation rounds and both
selection strategies to answer questions 1-4. For very low-resource settings,
we considered the five accents with the least recording hours.

We used various GPUs for our experiments: 6 RTX 800, and 4 A100. Training and
evaluation were conducted over a month. Our models have approximately 311
million trainable parameters. Each audio sample was normalized and processed
at a 16kHz sample rate. We used default parameters from the HuggingFace
library for each pretrained model.

Table 3:  We used Wav2Vec to conduct initial experiments across domains and
strategies to identify the best selection strategy. Models marked with ** are
used to demonstrate that our algorithm is model agnostic, utilizing the EU-
Most selection strategy, which has been proven the most effective. Our AL
experiments also use this strategy. Wav2Vec, using the random strategy, scored
0.1111, 0.3571, and 0.1666 for the general, clinical, and both domains,
respectively. We omit random results to enhance readability.

Model | General | Clinical | Both
---|---|---|---
Baseline | EU-Most | AL-EU-Most | Baseline | EU-Most | AL-EU-Most | Baseline | EU-Most | AL-EU-Most
Wav2vec | 0.4980 | 0.1011 | 0.1059 | 0.5610 | 0.2457 | 0.2545 | 0.5300 | 0.1266 | 0.1309
**Hubert | 0.1743 | 0.1901 | 0.1887 | 0.2907 | 0.2594 | 0.2709 | 0.2365 | 0.2453 | 0.2586
**WavLM | 0.1635 | 0.1576 | 0.1764 | 0.3076 | 0.2313 | 0.2537 | 0.2047 | 0.1897 | 0.1976
*Nemo | 0.2824 | 0.1765 | 0.1815 | 0.2600 | 0.2492 | 0.2526 | 0.3765 | 0.2576 | 0.2610
Average Performance | 0.2796 | 0.1563 | 0.1631 | 0.3548 | 0.2464 | 0.2579 | 0.3369 | 0.2043 | 0.2120
Whisper-Medium | 0.2806 | - | - | 0.3443 | - | - | 0.3116 | - | -

(a)

(b)

Figure 2: WER Performance on Accents from General Domain

(a)

(b)

Figure 3: WER Performance on Accents from Clinical Domain

(a)

(b)

Figure 4: WER Performance on Accents from Clinical+General (Both) Domain Table
4:  WER Evaluation Results on External Datasets, with $\alpha\in[0.60,0.65]$
as described in Section 3.1 and on Figure 1. We see an improvement for WER
using our approach in all datasets, showing that our algorithm is dataset-
agnostic.

Dataset | Split and Size for our approach | Finetuning Epochs | Baseline | EU-Most
---|---|---|---|---
$\mathcal{D}_{\mathrm{train}}^{*}$ | $\mathcal{D}_{\mathrm{pool}}$ | Top-k | Test | ($\mathcal{D}_{\mathrm{train}}$) | ($\mathcal{D}_{\mathrm{train}}^{*}$ \+ $\alpha\mathcal{D}_{\mathrm{pool}}$)
SautiDB [1] | 234 | 547 | 92 | 138 | 50 | 0.50 | 0.12
MedicalSpeech | 1598 | 3730 | 1333 | 622 | 5 | 0.30 | 0.28
CommonVoices English Accented Dataset (v10.0) [3] | 26614 | 62100 | 10350 | 232 | 5 | 0.50 | 0.22
Average | ✗ | ✗ | ✗ | ✗ | ✗ | 0.43 | 0.20

##  4 Results and Discussion

To compute the performance improvement for each domain, we take the ratio of
the average of our best strategy and the baseline.

Table 3 shows the results of our experiments, indicating that our uncertainty-
based selection approach significantly outperforms the baselines across all
models, domains, and datasets: general (55.90%), clinical (69.44%), and both
(61.74%). Our approach also surpasses Whisper-Medium [37, 39], demonstrating
the importance of epistemic uncertainty in ASR for low-resource languages. The
EU-Most selection strategy proves to be the most effective across all domains
due to the model exposure to highly uncertain samples, enhancing robustness
and performance. However, performance disparities between general and clinical
domains are noted, likely due to clinical samples complexity. These findings
confirm EU-Most as the superior selection strategy, as detailed in the results
and illustrated in Figures 2, 3, and 4. This answers question 2.

To identify the best learning signals within a diverse dataset characterized
by various accents, speaker traits, genders, and ages, we analyzed the top-k
uncertain accents using the EU-Most selection strategy. Our findings,
illustrated in Figures 2, 3, and 4, show that the top-10 accents (most
represented in recording hours) remained consistently challenging across all
rounds of analysis (refer to Figures 2, 3, 4 and Tables 6, 7, and 8). These
accents, characterized by high linguistic richness and variability, aid in
model learning and enhance performance over time. We positively answer
questions 1 and 3, confirming that the model adapts effectively to the
beneficial accents from all domains. This demonstrates that the model adapts
qualitatively and quantitatively well to the beneficial accents and benefits
from all domains. Figures 2 (b), 3 (b), and 4 (b) also affirm positive
outcomes for question 4, showing consistent improvement or stable performance
on low-resource accents. This highlights the relevance of our approach in
addressing the challenges associated with the low resource availability
typical of many African accents and languages.

To demonstrate the agnostic aspect of our approach, we evaluated it with three
additional pretrained models (Hubert, WavLM, and Nemo) and three datasets
containing accented speech in general and clinical domains, using only the EU-
Most selection strategy. The results, shown in Tables 3 and 4, indicate that
our uncertainty-based adaptation approach consistently outperforms baselines.
This confirms that our approach applies to any model architecture and dataset,
and allows us to answer positively question 5.

##  5 Conclusion

We combined several AL paradigms, the CSA, and EU to create a novel multi-
round adaptation process for high-performing pretrained speech models, aiming
to build efficient African-accented English ASR models. We introduced a new
metric called U-WER to track model adaptation to hard accents. Our experiments
showed a remarkable 69.44% WER improvement while reducing the data required
for effective training by approximately 45% compared to existing baselines.
This demonstrates our approach’s efficiency and potential to significantly
lower barriers to ASR technologies in underserved regions. Our method enhances
model robustness and generalization across various domains, datasets, and
accents, crucial for scalable ASR systems. This also helps mitigate bias in
ASR technologies, promoting more inclusive and fair AI applications.

##  6 Limitations

In discussing trade-offs (Section 4), we observed that while our approach
improves performance, especially with linguistically rich accents, it is
crucial for complex domains like the clinical and both domains to establish a
stopping criterion that balances adaptation rounds with the selection of new
data points each round. This balance is vital for refining our methodology.
Regarding computational complexity, our work faced limitations due to the
availability of computational resources. With better resources, we would
consider implementing Deep Ensembles [30] as an alternative to our current Mc-
Dropout method for estimating epistemic uncertainty.

## Acknowledgments and Disclosure of Funding

The author is very grateful to Ines Arous for her help in reviewing the
manuscript and suggesting very important changes to improve the quality of the
paper. The author acknowledges the support of the Mila Quebec AI Institute for
computing resources.

The author acknowledges Intron Health for providing the Afrispeech-200
dataset. The author is grateful to Atnafu Lambebo Tonja, Chris Chinenye
Emezue, Tobi Olatunji, Naome A Etori, Salomey Osei, Tosin Adewumi, and Sahib
Singh; for their help in the early stage of this project.

## References

  * Afonja et al. [2021a] T. Afonja, C. Mbataku, A. Malomo, O. Okubadejo, L. Francis, M. Nwadike, and I. Orife.  Sautidb: Nigerian accent dataset collection, 2021a.
  * Afonja et al. [2021b] T. Afonja, O. Mudele, I. Orife, K. Dukor, L. Francis, D. Goodness, O. Azeez, A. Malomo, and C. Mbataku.  Learning nigerian accent embeddings from speech: preliminary results based on sautidb-naija corpus.  _arXiv preprint arXiv:2112.06199_ , 2021b.
  * Ardila et al. [2019] R. Ardila, M. Branson, K. Davis, M. Henretty, M. Kohler, J. Meyer, R. Morais, L. Saunders, F. M. Tyers, and G. Weber.  Common voice: A massively-multilingual speech corpus.  _arXiv preprint arXiv:1912.06670_ , 2019.
  * Badenhorst and De Wet [2017] J. Badenhorst and F. De Wet.  The limitations of data perturbation for asr of learner data in under-resourced languages.  In _2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)_ , pages 44–49. IEEE, 2017.
  * Badenhorst and De Wet [2019] J. Badenhorst and F. De Wet.  The usefulness of imperfect speech data for asr development in low-resource languages.  _Information_ , 10(9):268, 2019.
  * Barnard et al. [2009] E. Barnard, M. Davel, and C. Van Heerden.  Asr corpus design for resource-scarce languages.  ISCA, 2009.
  * Benzeghiba et al. [2007] M. Benzeghiba, R. De Mori, O. Deroo, S. Dupont, T. Erbes, D. Jouvet, L. Fissore, P. Laface, A. Mertins, C. Ris, R. Rose, V. Tyagi, and C. Wellekens.  Automatic speech recognition and speech variability: A review.  _Speech Communication_ , 49(10):763–786, 2007.  ISSN 0167-6393.  doi: https://doi.org/10.1016/j.specom.2007.02.006.  URL https://www.sciencedirect.com/science/article/pii/S0167639307000404.  Intrinsic Speech Variations.
  * Chen et al. [2022] S. Chen, C. Wang, Z. Chen, Y. Wu, S. Liu, Z. Chen, J. Li, N. Kanda, T. Yoshioka, X. Xiao, et al.  Wavlm: Large-scale self-supervised pre-training for full stack speech processing.  _IEEE Journal of Selected Topics in Signal Processing_ , 16(6):1505–1518, 2022.
  * Chiu et al. [2018] C.-C. Chiu, A. Tripathi, K. Chou, C. Co, N. Jaitly, D. Jaunzeikare, A. Kannan, P. Nguyen, H. Sak, A. Sankar, J. Tansuwan, N. Wan, Y. Wu, and X. Zhang.  Speech Recognition for Medical Conversations.  In _Proc. Interspeech 2018_ , pages 2972–2976, 2018.  doi: 10.21437/Interspeech.2018-40.
  * Conneau et al. [2020] A. Conneau, A. Baevski, R. Collobert, A. rahman Mohamed, and M. Auli.  Unsupervised cross-lingual representation learning for speech recognition.  _INTERSPEECH_ , 2020.  doi: 10.21437/interspeech.2021-329.
  * Das et al. [2021] N. Das, S. Bodapati, M. Sunkara, S. Srinivasan, and D. H. Chau.  Best of both worlds: Robust accented speech recognition with adversarial transfer learning.  _arXiv preprint arXiv:2103.05834_ , 2021.
  * DiChristofano et al. [2022] A. DiChristofano, H. Shuster, S. Chandra, and N. Patwari.  Performance disparities between accents in automatic speech recognition.  _arXiv preprint arXiv:2208.01157_ , 2022.
  * Dossou and Emezue [2021] B. F. Dossou and C. C. Emezue.  Okwugb$\backslash$’e: End-to-end speech recognition for fon and igbo.  _arXiv preprint arXiv:2103.07762_ , 2021.
  * Dossou et al. [2022] B. F. Dossou, A. L. Tonja, O. Yousuf, S. Osei, A. Oppong, I. Shode, O. O. Awoyomi, and C. C. Emezue.  Afrolm: A self-active learning-based multilingual pretrained language model for 23 african languages.  _arXiv preprint arXiv:2211.03263_ , 2022.
  * Ducoffe and Precioso [2018] M. Ducoffe and F. Precioso.  Adversarial active learning for deep networks: a margin based approach.  _arXiv preprint arXiv:1802.09841_ , 2018.
  * Eberhard et al. [2019] D. Eberhard, G. Simons, and C. Fennig.  _Ethnologue: Languages of the World, 22nd Edition_.  02 2019.
  * Finley et al. [2018] G. Finley, W. Salloum, N. Sadoughi, E. Edwards, A. Robinson, N. Axtmann, M. Brenndoerfer, M. Miller, and D. Suendermann-Oeft.  From dictations to clinical reports using machine translation.  In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)_ , pages 121–128, 2018.
  * Gal and Ghahramani [2016] Y. Gal and Z. Ghahramani.  Dropout as a bayesian approximation: Representing model uncertainty in deep learning.  In _international conference on machine learning_ , pages 1050–1059. PMLR, 2016.
  * Gal et al. [2017] Y. Gal, R. Islam, and Z. Ghahramani.  Deep Bayesian active learning with image data.  In D. Precup and Y. W. Teh, editors, _Proceedings of the 34th International Conference on Machine Learning_ , volume 70 of _Proceedings of Machine Learning Research_ , pages 1183–1192. PMLR, 06–11 Aug 2017.  URL https://proceedings.mlr.press/v70/gal17a.html.
  * Gulati et al. [2020] A. Gulati, J. Qin, C.-C. Chiu, N. Parmar, Y. Zhang, J. Yu, W. Han, S. Wang, Z. Zhang, Y. Wu, et al.  Conformer: Convolution-augmented transformer for speech recognition.  _arXiv preprint arXiv:2005.08100_ , 2020.
  * Hakkani-Tür et al. [2002] D. Hakkani-Tür, G. Riccardi, and A. Gorin.  Active learning for automatic speech recognition.  In _2002 IEEE International Conference on Acoustics, Speech, and Signal Processing_ , volume 4, pages IV–3904–IV–3907, 2002.  doi: 10.1109/ICASSP.2002.5745510.
  * Hinsvark et al. [2021] A. Hinsvark, N. Delworth, M. D. Rio, Q. McNamara, J. Dong, R. Westerman, M. Huang, J. Palakapilly, J. Drexler, I. Pirkin, N. Bhandari, and M. Jette.  Accented speech recognition: A survey, 2021.
  * Houlsby et al. [2011] N. Houlsby, F. Huszár, Z. Ghahramani, and M. Lengyel.  Bayesian active learning for classification and preference learning.  _arXiv preprint arXiv:1112.5745_ , 2011.
  * Hsu et al. [2021] W.-N. Hsu, B. Bolte, Y.-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed.  Hubert: Self-supervised speech representation learning by masked prediction of hidden units.  _IEEE/ACM Transactions on Audio, Speech, and Language Processing_ , 29:3451–3460, 2021.
  * Kendall and Gal [2017] A. Kendall and Y. Gal.  What uncertainties do we need in bayesian deep learning for computer vision?  _Advances in Neural Information Processing Systems_ , 30:5574–5584, 2017.
  * Kirsch et al. [2019] A. Kirsch, J. v. Amersfoort, and Y. Gal.  _BatchBALD: efficient and diverse batch acquisition for deep Bayesian active learning_.  Curran Associates Inc., Red Hook, NY, USA, 2019.
  * Kodish-Wachs et al. [2018] J. Kodish-Wachs, E. Agassi, P. Kenny III, and J. M. Overhage.  A systematic comparison of contemporary automatic speech recognition engines for conversational clinical speech.  In _AMIA Annual Symposium Proceedings_ , volume 2018, page 683. American Medical Informatics Association, 2018.
  * Koenecke [2021] A. Koenecke.  Racial Disparities in Automated Speech Recognition.  In _Proc. 2021 ISCA Symposium on Security and Privacy in Speech Communication_ , 2021.
  * Koenecke et al. [2020] A. Koenecke, A. Nam, E. Lake, J. Nudell, M. Quartey, Z. Mengesha, C. Toups, J. R. Rickford, D. Jurafsky, and S. Goel.  Racial disparities in automated speech recognition.  _Proceedings of the National Academy of Sciences_ , 117(14):7684–7689, 2020.
  * Lakshminarayanan et al. [2017] B. Lakshminarayanan, A. Pritzel, and C. Blundell.  Simple and scalable predictive uncertainty estimation using deep ensembles.  _Advances in neural information processing systems_ , 30, 2017.
  * Liu and Li [2023] S. Liu and X. Li.  Understanding uncertainty sampling.  _arXiv preprint arXiv:2307.02719_ , 2023.
  * Mehrabi et al. [2021] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan.  A survey on bias and fairness in machine learning.  _ACM Comput. Surv._ , 54(6), jul 2021.  ISSN 0360-0300.  doi: 10.1145/3457607.  URL https://doi.org/10.1145/3457607.
  * Mengesha et al. [2021] Z. Mengesha, C. Heldreth, M. Lahav, J. Sublewski, and E. Tuennerman.  “i don’t think these devices are very culturally sensitive.”—impact of automated speech recognition errors on african americans.  _Frontiers in Artificial Intelligence_ , page 169, 2021.
  * Mitchell et al. [2019] M. Mitchell, S. Wu, A. Zaldivar, P. Barnes, L. Vasserman, B. Hutchinson, E. Spitzer, I. D. Raji, and T. Gebru.  Model cards for model reporting.  In _Proceedings of the Conference on Fairness, Accountability, and Transparency_ , FAT* ’19, page 220–229, New York, NY, USA, 2019. Association for Computing Machinery.  ISBN 9781450361255.  doi: 10.1145/3287560.3287596.  URL https://doi.org/10.1145/3287560.3287596.
  * Nallasamy et al. [2012] U. Nallasamy, F. Metze, and T. Schultz.  Active learning for accent adaptation in automatic speech recognition.  In _2012 IEEE Spoken Language Technology Workshop (SLT)_ , pages 360–365, 2012.  doi: 10.1109/SLT.2012.6424250.
  * Olatunji et al. [2023a] T. Olatunji, T. Afonja, B. F. P. Dossou, A. L. Tonja, C. C. Emezue, A. M. Rufai, and S. Singh.  AfriNames: Most ASR Models "Butcher" African Names.  In _Proc. INTERSPEECH 2023_ , pages 5077–5081, 2023a.  doi: 10.21437/Interspeech.2023-2122.
  * Olatunji et al. [2023b] T. Olatunji, T. Afonja, A. Yadavalli, C. C. Emezue, S. Singh, B. F. P. Dossou, J. Osuchukwu, S. Osei, A. L. Tonja, N. A. Etori, and C. Mbataku.  Afrispeech-200: Pan-african accented speech dataset for clinical and general domain asr.  2023b.  URL https://api.semanticscholar.org/CorpusID:263334123.
  * Radford et al. [2022] A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever.  Robust speech recognition via large-scale weak supervision, 2022.
  * Radford et al. [2023] A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever.  Robust speech recognition via large-scale weak supervision.  In _International Conference on Machine Learning_ , pages 28492–28518. PMLR, 2023.
  * Riccardi and Hakkani-Tur [2005] G. Riccardi and D. Hakkani-Tur.  Active learning: theory and applications to automatic speech recognition.  _IEEE Transactions on Speech and Audio Processing_ , 13(4):504–511, 2005.  doi: 10.1109/TSA.2005.848882.
  * Selbst et al. [2019] A. D. Selbst, D. Boyd, S. A. Friedler, S. Venkatasubramanian, and J. Vertesi.  Fairness and abstraction in sociotechnical systems.  In _Proceedings of the Conference on Fairness, Accountability, and Transparency_ , FAT* ’19, page 59–68, New York, NY, USA, 2019. Association for Computing Machinery.  ISBN 9781450361255.  doi: 10.1145/3287560.3287598.  URL https://doi.org/10.1145/3287560.3287598.
  * Sener and Savarese [2017] O. Sener and S. Savarese.  Active learning for convolutional neural networks: A core-set approach.  _arXiv preprint arXiv:1708.00489_ , 2017.
  * Settles [2009] B. Settles.  Active learning literature survey.  2009\.  URL https://api.semanticscholar.org/CorpusID:324600.
  * Tsvetkov [2017] Y. Tsvetkov.  Opportunities and challenges in working with low-resource languages.  In _Carnegie Mellon Univ., Language Technologies Institute_ , 2017.
  * Yemmene and Besacier [2019] P. Yemmene and L. Besacier.  Motivations, challenges, and perspectives for the development of an automatic speech recognition system for the under-resourced ngiemboon language.  In _Proceedings of The First International Workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) co-located with ICNLSP 2019-Short Papers_ , pages 59–67, 2019.
  * Zapata and Kirkedal [2015] J. Zapata and A. S. Kirkedal.  Assessing the performance of automatic speech recognition systems when used by native and non-native speakers of three major languages in dictation workflows.  In _Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015)_ , pages 201–210, 2015.

## Checklist

  1. 1.

For all authors…

    1. (a)

Do the main claims made in the abstract and introduction accurately reflect
the paper’s contributions and scope? [Yes]  (See Section 4)

    2. (b)

Did you describe the limitations of your work? [Yes]  (See Section 6)

    3. (c)

Did you discuss any potential negative societal impacts of your work? [N/A]

    4. (d)

Have you read the ethics review guidelines and ensured that your paper
conforms to them? [Yes]

  2. 2.

If you are including theoretical results…

    1. (a)

Did you state the full set of assumptions of all theoretical results? [N/A]

    2. (b)

Did you include complete proofs of all theoretical results? [N/A]

  3. 3.

If you ran experiments (e.g. for benchmarks)…

    1. (a)

Did you include the code, data, and instructions needed to reproduce the main
experimental results (either in the supplemental material or as a URL)? [Yes]
(See the abstract)

    2. (b)

Did you specify all the training details (e.g., data splits, hyperparameters,
how they were chosen)? [Yes]  (See Tables 2, 1, A.1)

    3. (c)

Did you report error bars (e.g., with respect to the random seed after running
experiments multiple times)? [Yes]  (See Section 4, and Figures 2, 3 and 4)

    4. (d)

Did you include the total amount of compute and the type of resources used
(e.g., type of GPUs, internal cluster, or cloud provider)? [Yes]  (See Section
3.3)

  4. 4.

If you are using existing assets (e.g., code, data, models) or
curating/releasing new assets…

    1. (a)

If your work uses existing assets, did you cite the creators? [Yes]  (See
Section 3.1)

    2. (b)

Did you mention the license of the assets? [Yes]  (See Section 3.1)

    3. (c)

Did you include any new assets either in the supplemental material or as a
URL? [N/A]

    4. (d)

Did you discuss whether and how consent was obtained from people whose data
you’re using/curating? [N/A]

    5. (e)

Did you discuss whether the data you are using/curating contains personally
identifiable information or offensive content? [N/A]

  5. 5.

If you used crowdsourcing or conducted research with human subjects…

    1. (a)

Did you include the full text of instructions given to participants and
screenshots, if applicable? [N/A]

    2. (b)

Did you describe any potential participant risks, with links to Institutional
Review Board (IRB) approvals, if applicable? [N/A]

    3. (c)

Did you include the estimated hourly wage paid to participants and the total
amount spent on participant compensation? [N/A]

##  Appendix A Appendices

###  A.1 Hyper-parameters

Table 5 shows the hyper-parameter settings used in this study. The top-k value
in the table is changed according to the domain used in each of the
experiments. For example, when conducting experiments in the general domain,
we set the value of top-k to 2k.

Hyper-parameters | Values
---|---
attention dropout | 0.1
hidden dropout | 0.1
layer drop | 0.1
train batch size | 16
val batch size | 8
number of epochs | 5
learning rate | 3e-4
maximum audio length | 260000
maximum label length | 260
minimum transcript length | 10
top_k | 2000, 3500, 6500
domains | general, clinical, all
active learning rounds | 3
sampling mode | EU-Most, random
Mc-dropout round | 10
Table 5: Hyper-parameters summary

###  A.2 Country Statistics

Table 6 shows the statistics of the countries across the AfriSpeech-200
dataset.

Country | Clips | Speakers | Duration (seconds) | Duration (hrs)
---|---|---|---|---
Nigeria | 45875 | 1979 | 512646.88 | 142.40
Kenya | 8304 | 137 | 75195.43 | 20.89
South Africa | 7870 | 223 | 81688.11 | 22.69
Ghana | 2018 | 37 | 18581.13 | 5.16
Botswana | 1391 | 38 | 14249.01 | 3.96
Uganda | 1092 | 26 | 10420.42 | 2.89
Rwanda | 469 | 9 | 5300.99 | 1.47
United States of America | 219 | 5 | 1900.98 | 0.53
Turkey | 66 | 1 | 664.01 | 0.18
Zimbabwe | 63 | 3 | 635.11 | 0.18
Malawi | 60 | 1 | 554.61 | 0.15
Tanzania | 51 | 2 | 645.51 | 0.18
Lesotho | 7 | 1 | 78.40 | 0.02
Table 6: Countries Statistics across the dataset

###  A.3 Dataset Accents Stats

Tables 7 and 8 provide a list of AfriSpeech accents along with the number of
unique speakers, countries where speakers for each accent are located,
duration in seconds for each accent, and their presence in the train, dev, and
test splits.

Accent | Clips | Speakers | Duration(s) | Countries | Splits
---|---|---|---|---|---
yoruba | 15407 | 683 | 161587.55 | US,NG | train,test,dev
igbo | 8677 | 374 | 93035.79 | US,NG,ZA | train,test,dev
swahili | 6320 | 119 | 55932.82 | KE,TZ,ZA,UG | train,test,dev
hausa | 5765 | 248 | 70878.67 | NG | train,test,dev
ijaw | 2499 | 105 | 33178.9 | NG | train,test,dev
afrikaans | 2048 | 33 | 20586.49 | ZA | train,test,dev
idoma | 1877 | 72 | 20463.6 | NG | train,test,dev
zulu | 1794 | 52 | 18216.97 | ZA,TR,LS | dev,train,test
setswana | 1588 | 39 | 16553.22 | BW,ZA | dev,test,train
twi | 1566 | 22 | 14340.12 | GH | test,train,dev
isizulu | 1048 | 48 | 10376.09 | ZA | test,train,dev
igala | 919 | 31 | 9854.72 | NG | train,test
izon | 838 | 47 | 9602.53 | NG | train,dev,test
kiswahili | 827 | 6 | 8988.26 | KE | train,test
ebira | 757 | 42 | 7752.94 | NG | train,test,dev
luganda | 722 | 22 | 6768.19 | UG,BW,KE | test,dev,train
urhobo | 646 | 32 | 6685.12 | NG | train,dev,test
nembe | 578 | 16 | 6644.72 | NG | train,test,dev
ibibio | 570 | 39 | 6489.29 | NG | train,test,dev
pidgin | 514 | 20 | 5871.57 | NG | test,train,dev
luhya | 508 | 4 | 4497.02 | KE | train,test
kinyarwanda | 469 | 9 | 5300.99 | RW | train,test,dev
xhosa | 392 | 12 | 4604.84 | ZA | train,dev,test
tswana | 387 | 18 | 4148.58 | ZA,BW | train,test,dev
esan | 380 | 13 | 4162.63 | NG | train,test,dev
alago | 363 | 8 | 3902.09 | NG | train,test
tshivenda | 353 | 5 | 3264.77 | ZA | test,train
fulani | 312 | 18 | 5084.32 | NG | test,train
isoko | 298 | 16 | 4236.88 | NG | train,test,dev
akan (fante) | 295 | 9 | 2848.54 | GH | train,dev,test
ikwere | 293 | 14 | 3480.43 | NG | test,train,dev
sepedi | 275 | 10 | 2751.68 | ZA | dev,test,train
efik | 269 | 11 | 2559.32 | NG | test,train,dev
edo | 237 | 12 | 1842.32 | NG | train,test,dev
luo | 234 | 4 | 2052.25 | UG,KE | test,train,dev
kikuyu | 229 | 4 | 1949.62 | KE | train,test,dev
bekwarra | 218 | 3 | 2000.46 | NG | train,test
isixhosa | 210 | 9 | 2100.28 | ZA | train,dev,test
hausa/fulani | 202 | 3 | 2213.53 | NG | test,train
epie | 202 | 6 | 2320.21 | NG | train,test
isindebele | 198 | 2 | 1759.49 | ZA | train,test
venda and xitsonga | 188 | 2 | 2603.75 | ZA | train,test
sotho | 182 | 4 | 2082.21 | ZA | dev,test,train
akan | 157 | 6 | 1392.47 | GH | test,train
nupe | 156 | 9 | 1608.24 | NG | dev,train,test
anaang | 153 | 8 | 1532.56 | NG | test,dev
english | 151 | 11 | 2445.98 | NG | dev,test
afemai | 142 | 2 | 1877.04 | NG | train,test
shona | 138 | 8 | 1419.98 | ZA,ZW | test,train,dev
eggon | 137 | 5 | 1833.77 | NG | test
luganda and kiswahili | 134 | 1 | 1356.93 | UG | train
ukwuani | 133 | 7 | 1269.02 | NG | test
sesotho | 132 | 10 | 1397.16 | ZA | train,dev,test
benin | 124 | 4 | 1457.48 | NG | train,test
kagoma | 123 | 1 | 1781.04 | NG | train
nasarawa eggon | 120 | 1 | 1039.99 | NG | train
tiv | 120 | 14 | 1084.52 | NG | train,test,dev
south african english | 119 | 2 | 1643.82 | ZA | train,test
borana | 112 | 1 | 1090.71 | KE | train
Table 7: Dataset Accent Stats, Part I Accent | Clips | Speakers | Duration(s) | Countries | Splits
---|---|---|---|---|---
swahili ,luganda ,arabic | 109 | 1 | 929.46 | UG | train
ogoni | 109 | 4 | 1629.7 | NG | train,test
mada | 109 | 2 | 1786.26 | NG | test
bette | 106 | 4 | 930.16 | NG | train,test
berom | 105 | 4 | 1272.99 | NG | dev,test
bini | 104 | 4 | 1499.75 | NG | test
ngas | 102 | 3 | 1234.16 | NG | train,test
etsako | 101 | 4 | 1074.53 | NG | train,test
okrika | 100 | 3 | 1887.47 | NG | train,test
venda | 99 | 2 | 938.14 | ZA | train,test
siswati | 96 | 5 | 1367.45 | ZA | dev,train,test
damara | 92 | 1 | 674.43 | NG | train
yoruba, hausa | 89 | 5 | 928.98 | NG | test
southern sotho | 89 | 1 | 889.73 | ZA | train
kanuri | 86 | 7 | 1936.78 | NG | test,dev
itsekiri | 82 | 3 | 778.47 | NG | test,dev
ekpeye | 80 | 2 | 922.88 | NG | test
mwaghavul | 78 | 2 | 738.02 | NG | test
bajju | 72 | 2 | 758.16 | NG | test
luo, swahili | 71 | 1 | 616.57 | KE | train
dholuo | 70 | 1 | 669.07 | KE | train
ekene | 68 | 1 | 839.31 | NG | test
jaba | 65 | 2 | 540.66 | NG | test
ika | 65 | 4 | 576.56 | NG | test,dev
angas | 65 | 1 | 589.99 | NG | test
ateso | 63 | 1 | 624.28 | UG | train
brass | 62 | 2 | 900.04 | NG | test
ikulu | 61 | 1 | 313.2 | NG | test
eleme | 60 | 2 | 1207.92 | NG | test
chichewa | 60 | 1 | 554.61 | MW | train
oklo | 58 | 1 | 871.37 | NG | test
meru | 58 | 2 | 865.07 | KE | train,test
agatu | 55 | 1 | 369.11 | NG | test
okirika | 54 | 1 | 792.65 | NG | test
igarra | 54 | 1 | 562.12 | NG | test
ijaw(nembe) | 54 | 2 | 537.56 | NG | test
khana | 51 | 2 | 497.42 | NG | test
ogbia | 51 | 4 | 461.15 | NG | test,dev
gbagyi | 51 | 4 | 693.43 | NG | test
portuguese | 50 | 1 | 525.02 | ZA | train
delta | 49 | 2 | 425.76 | NG | test
bassa | 49 | 1 | 646.13 | NG | test
etche | 49 | 1 | 637.48 | NG | test
kubi | 46 | 1 | 495.21 | NG | test
jukun | 44 | 2 | 362.12 | NG | test
igbo and yoruba | 43 | 2 | 466.98 | NG | test
urobo | 43 | 3 | 573.14 | NG | test
kalabari | 42 | 5 | 305.49 | NG | test
ibani | 42 | 1 | 322.34 | NG | test
obolo | 37 | 1 | 204.79 | NG | test
idah | 34 | 1 | 533.5 | NG | test
bassa-nge/nupe | 31 | 3 | 267.42 | NG | test,dev
yala mbembe | 29 | 1 | 237.27 | NG | test
eket | 28 | 1 | 238.85 | NG | test
afo | 26 | 1 | 171.15 | NG | test
ebiobo | 25 | 1 | 226.27 | NG | test
nyandang | 25 | 1 | 230.41 | NG | test
ishan | 23 | 1 | 194.12 | NG | test
bagi | 20 | 1 | 284.54 | NG | test
estako | 20 | 1 | 480.78 | NG | test
gerawa | 13 | 1 | 342.15 | NG | test
Table 8: Dataset Accent Stats, Part II

###  A.4 Most common accent distribution

Figures 5 and 6 show the most common accent distribution across the general
domain with both random and EU-Most selection strategies.

Figure 5: Most common accents distribution across the general domain with EU-
Most sampling strategy. Figure 6: Most common accents distribution across the
general domain with random selection strategy.

###  A.5 Ascending and Descending Accents

Figure 7 shows ascending and descending accents across the Top 2k most
uncertain samples.

Figure 7: Ascending and descending accents across Top-2K most uncertain
samples.