# Automatic Speech recognition for Speech Assessment of Preschool Children

Amirhossein Abaskohi, Fatemeh Mortazavi, Hadi Moradi
University of Tehran
Tehran
{amir.abaskohi, mortazavi.fatemeh<EMAIL_ADDRESS>

###### Abstract

The acoustic and linguistic features of preschool speech are investigated in
this study to design an automated speech recognition (ASR) system. Acoustic
fluctuation has been highlighted as a significant barrier to developing high-
performance ASR applications for youngsters. Because of the epidemic,
preschool speech assessment should be conducted online. Accordingly, there is
a need for an automatic speech recognition system. We were confronted with new
challenges in our cognitive system, including converting meaningless words
from speech to text and recognizing word sequence. After testing and
experimenting with several models we obtained a 3.1% phoneme error rate in
Persian. Wav2Vec 2.0 is a paradigm that could be used to build a robust end-
to-end speech recognition system.

_K_ eywords Speech Recognition $\cdot$ Machine Learning $\cdot$ Deep Learning

##  1 Introduction

Automatic speech recognition (ASR) for children is a complex topic with many
applications. Although there is a substantial body of work comparing the
acoustic and linguistic features of children and adults [1, 2], our knowledge
of how these variations impact speech recognition performance is limited. We
will compare several models in our tasks in this article and share the dataset
we used to train or fine-tune for Persian.

Adult speech recognition has increased substantially in recent years, but
there has been more minor improvement in recognizing speech generated by
children [3, 4]. Identifying children’s speech is challenging for a variety of
reasons. Children’s capacity to correctly recognize speech sounds varies as
they learn to talk[5, 6]. Children’s general speaking pace is slower, and
their speaking rate, vocal effort, and spontaneity of speech are more
variable[7]. Although there are language distinctions, addressing them is
pointless because they have no bearing on the duties. It has been demonstrated
that by training directly on children’s speech, this disparity in performance
may be minimized on a digit identification test, albeit accuracy is still
inferior to that of adults[8].

Because the fundamental frequency and formant bandwidths are of equal scale,
speech recognition is more challenging for children’s voices. Furthermore,
there are more significant difficulties in the words sequence and meaningless
words task that we have utilized in our assessments.

The spectral and temporal oral variability in children’s speech is a key
stumbling block in acoustic modeling of children’s speech. Because formant
values are more variable in children than in adults, there is more overlap
across phonemic classes, making the categorization task intrinsically more
complex[7]. Furthermore, most acoustic characteristics have a significantly
comprehensive range of values for children than adults. Five-year-old
children, for example, have formant values that are up to 50% greater than
male adults[1].ASR performance can be severely harmed by a broad acoustic
parameter range combined with increased acoustic unpredictability.

It is critical to have a thorough knowledge of children’s skills before
starting school. The purpose of the current study is to identify their
strengths and shortcomings, which leads to a tailored and personalized
education system. The phonological and acoustic test is one of the abilities
that should be examined. However, to increase speed and automation, this
evaluation should be performed using an ASR.

In our system, ASR is required for two tests. The first test is called Rapid
Automatic Naming(RAN). The RAN task is a behavioral test that evaluates how
fast and accurately people name groups of visual stimuli. For fluent naming of
a sequence of visual stimuli, RAN depends on the coordination of various
processes into a synchronized access and retrieval mechanism (Fig. 1)[9]. The
difficulty in this activity includes a word sequence, as well as the necessity
of correctness because the findings will be used to assess children’s ability
to talk. In addition, due to the importance of speed in this activity, the
model should provide accurate high-speed utterances.

Figure 1: Rapid Automatic Naming Test.

The second test is a phonological memory test, in which youngsters are asked
to listen to a meaningless word and then repeat it. This exam is significant
complex because it demonstrates strong developmental connections between test
results and vocabulary, reading, and overall abilities in young children[10].
The task’s primary difficulty is the great degree of similarity between these
words and the actual words. For example "mashogh" is like "ghashogh"(spoon)
except the first letter and the first phoneme is different. These similarities
make classification hard. It is crucial to note that accuracy is essential in
this test since it focuses on the children’s listening skills, and they must
repeat what they hear in the test rather than the term that exists in reality.

##  2 Previous Work

There have been several techniques to enhance speech recognition in children
and other languages, including Persian in our case. We start with an overview
of English language procedures, then go on to approaches for children, and
ultimately to Persian language approaches, as our tasks need us to deal with
languages other than English as well as youngsters.

###  2.1 English

Artificial Neural Networks(ANNs) have been used in ASR since the late 1980s.
Isolated word identification and phoneme recognition using a Time Delay Neural
Network (TDNN) are examples of major work in this subject[11]. However, ANNs
were ignored owing to their flaws and were rapidly abandoned.

Mohamed et al.[12] published a deep ANN-HMM hybrid model for phoneme
identification in 2009, which sparked renewed interest in constructing ANN-
based ASR systems. In their research, they employed the same type of ANN-HMM
as in the early 1990s, but instead of MLP, they used a DNN. The cross-entropy
criteria was used to train the suggested model. On the TIMIT test set, this
model produced a Phoneme Error Rate (PER) of 23%, which was much better than
the 27.7% and 25.6% PER achieved by a hybrid mono-phone and tri-phone Gaussian
mixture model (GMM)-HMM.

It was discovered that utilizing a DNN for feature extraction is more
efficient than using a shallow network in a study done in 2009 and 2010 in
partnership with researchers from the University of Toronto and MSR. Also, as
compared to Mel Frequency Cepstral Coefficients (MFCC) features, using
spectrogram features enhances outcomes[13].

In Seide at al.[14] the CD-DNN-HMM model introduced in [15] was applied to the
Switchboard benchmark dataset in this work. On the HUB500 evaluation set, this
model achieved a 16.1% Word Error Rate (WER) after being trained using the
cross-entropy criteria. This model’s effectiveness may be attributed to the
following factors: (1) the use of deep structure (2) the representation of
tri-phones (3) the usage of the features’ contextual window as input data.

Following the comeback of DNNs in ASR in 2009, the most recent deep
architecture was based on RNNs, namely the LSTM network. DLSTM was employed
for phoneme identification in articles from 2013 [16, 17] by Graves, and the
findings revealed that using this network resulted in the lowest PER on the
TIMIT dataset up to that point.

In 2014, IBM researchers used a mix of convolutional neural networks (CNN) and
deep neural networks (DNN), as well as i-Vector-based adaption approaches, to
lower the WER on the HUB500 evaluation set to 10.4%, which was solely
attributable to the AM improvement. The English conversational telephone ASR
system was created by IBM researchers, which took use of advances in deep
learning-based ASR systems[18]. On the Switchboard section of the Hub5-2000
assessment set, this model received an 8% WER. The maxout network RNNs, as
well as CNNs using a deep learning technique, were utilized in the suggested
method.

Better results were obtained in subsequent studies employing deeper
convolutional layers and recurrent layers. In 2019, [19] proposes an end-to-
end concept with deep architecture. This model has 54 convolutional layers
reached a WER of 2.95% on LibriSpeech.

The Facebook AI team developed a model named "Wav2Vec 2.0" in a paper released
in 2020, which first encodes speech using many blocks comprising a temporal
convolution, followed by layer normalization and GELU activation function. The
model then uses transformers to build contextualized representations. Finally,
the quantization module is used to construct the final product[20]. They
achieved 2.1% WER on LibriSpeech.

###  2.2 Children

Gray et al.[21] presents an LVCSR system that is utilized for collaboration
with electronic gadgets. The ASR technique they developed increases the
accuracy of youngsters speaking US English to living room electronic devices
(LREDs), such as a voice-controlled TV or computer. Vocal tract length
normalization, acoustic model adaptation, language model adaptation using kid-
specific content lists and grammars, and a neural network-based approach to
automatically identify child data are among the techniques they investigated
in their article. The model reached WER of 27.2%.

Ghai and Sinha et al.[22] imply that the conventional Mel spaced filter banks
for generating recognition features aren’t up to the task. They noticed
aberrations in the lower frequency filters in high-pitched speech and
suggested extending the filter bandwidth to smooth them out. MFCC features and
a weaker Gaussian mixture model(GMM) were used in their trials, which produced
good results.

Liao et al.[23], investigated that speech recognition for children is not only
a concern, but it is the expectation of an endless vocabulary system. They
developed a deployable LVCSR system for children based on this concept. They
used samples from Voice Search dataset to enhance children’s voice
recognition. They captured wide-band audio to account for the lengths of
children’s voice tracts so that reasonable estimations of high-frequency
filter banks may be established. They created a kid speech DNN classifier to
do the filtering. To achieve a child-friendly experience, they had two aims
for the language model: reduce the risk of objectionable mis-recognitions and
better simulate the sorts of inquiries that children were likely to utter. In
the end, they used some simple models like LSTM and CLDNN to reach their
goals. The final result they have reached is 9.4% and 20.0% word WER in clean
and noisy training conditions.

###  2.3 Persian

After the release of the Farsdat speech dataset in 1996[24], Persian ASR
research became more serious.Research Center of Intelligent Signal Processing
(RCISP) released the initial version of the Shenava ASR, which was built on a
neural network, in 2001[25]. This system had a 60% accuracy rate for
continuous voice recognition.

On the basis of the HMM, Sameti et al.[26] has introduced Nevisa. In a normal
environment, the suggested model’s accuracy for continuous speech recognition
was 75%. In [27], the second iteration of the Nevisa was released, achieving a
95% accuracy rate for continuous speech recognition from independent speakers.

In 2013, Azadi et al.[28] employed the MLP network to recognize Persian
speech, and the suggested model was tested using the Farsdat dataset.

In 2016, On the Farsdat dataset, Daneshvar et al.[29] employed DLSTM with a
Connectionist Temporal Classification (CTC) output layer for Persian phoneme
detection in 2016. CTC is an error signal ratio computation algorithm based on
a specified goal function. CTC maximizes the chance of reaching the target
sequence through network output for an input sequence x with T timesteps and a
target sequence 1 with length $Q\leq QT$.

Another work done in 2016 was for Babali et al.[30]. They used Kaldi-DNN [31]
framework which is a a Persian Acounstic Model (AM) is built on the Farsdat
dataset. Kaldi-DNN is a GMM-based acoustic model with HMM topology.

In 2020, Veisi et al.[32] applied a mix of deep belief networks (DBN) and Deep
Bidirectional Long Short-Term Memory (DBLSTM) with Connectionist Temporal
Classification (CTC) output layers for the acoustic model on the Farsdat
dataset for the first time.The DBN, which was employed in their model,
generated 39 unique properties for each frame. With 200 blocks and a learning
rate of 0.0005, they were able to achieve 16.7% PER.

##  3 Proposed method

As previously stated, our system included a variety of speech recognition
tasks, each with its unique set of characteristics. Although the various
models will be reviewed in this section, the dataset that was acquired for
these tasks will be described. In subsequent, the dataset will be introduced,
followed by a discussion of each problem and our solutions.

###  3.1 Dataset

Because these tasks contain certain specific words and should be utilized with
youngsters, we need to fine-tune our model on our specific dataset.
Furthermore, because the challenge comprises meaningless words, providing the
model with this data is crucial.

Data collection was conducted by asking some adults from social media and some
students from an elementary school to participate in our experiment.

Table.1 shows the number of data gathered for recognizing each color. Due to
the fact that two words are used for black in Persian, the number of black
samples is more. In addition, because the color recognition is a RAN task, a
sequence of data has been gathered. Table.2 depicts the number of sequence
data for colors. For the meaningless words, 12 voices have been gathered on
average for each word (there are 40 meaningless words in this task).

Table 1: Colors Data. Color | Number of Data Gathered
---|---
Blue | 483
Red | 482
Black | 873
Green | 472
Yellow | 488
Table 2: Colors Sequence Data. Environment | Number of Data Gathered
---|---
Clean | 90
Noisy | 24
Figure 2: The architecture of model we had used for RAN which contains VAD and
CNN classifier. Figure 3: The convolutional neural network architecture that
we have used for color and meaningless word detection.

###  3.2 Rapid automatic naming task

In this task the youngster should name a sequence of images he sees. Since the
outcome is crucial in evaluating the kid, classifying the entire sequence is
insufficient; each phase must be analyzed.

We employed a mix of Vocal Activity Detection (VAD) and a classifier as one of
our strategies(Fig.2).

Mel-frequency Cepstral coefficients(MFCCs) were employed to extract features
for the classifier. The classification was then done using a convolutional
neural network(Fig.3). Three 1D convolutional layers with 0.3 dropout, two
dense fully connected layers with 0.3 dropout, and a softmax layer are
utilized in this network. Despite the fact that this network achieved a test
set accuracy of 92%, there are issues with utilizing it to evaluate children.
The main problem is the accuracy of VAD.

Noise has a big impact on voice activity detection (Fig.4). In addition, the
classifier was able to recognize which color was said, but determining whether
the word was a color or not was extremely difficult and required a large
amount of data. Consequently, we decided to combine an ASR (which will be
discussed further) with a text evaluation to get a better assessment of the
youngster.

###  3.3 Meaningless words task

In this task, the youngster must repeat a meaningless word that the website
plays. These useless words are similar to meaningful words but with different
phonemes (for example "Sacaroni" instead of "Macaroni").

This task is distinct from the previous one. There is no sequence here, and
the output can be handled by a simple classifier. We trained a classifier
similar to Fig.3 and attained a 90% accuracy rate. The model was powerful
enough to identify the word, but it was not powerful enough to determine
whether the word is in the valid words or not. Adding a new class for out of
the set words didn’t assist because the desired words were spread out and a
term that sounded similar to one of them may result in one of the desired
words.

As a result, we applied ASR for this task and assessed the kid using the text
provided by the ASR model.

Figure 4: VAD is sensitive to noise. Some selected parts are not a valid word.

###  3.4 ASR approach

Figure 5: Wav2Vec 2.0 is a speech recognition system that learns both
contextualized speech representations and a database of discretized speech
units[20].

As previously noted, we employed Wav2Vec 2.0 ASR model because it can be
trained on a limited data sample[20].

Alexei Baevski of the Facebook AI team revealed Wav2Vec 2.0, a state-of-the-
art model that has been trained on approximately 960 hours of datasets such as
Common Voice[33] and LibriSpeech[34]. Convolutional feature encoding,
quantized representation, and a transformer model are used in this
model(Fig.5).

Transformer-based neural networks have revolutionized natural language
processing, but they are just now gaining traction in the voice processing
industry. This is set to change in Wav2vec 2.0. Its design is based on the
Transformer’s encoder, and it has a training goal that is comparable to BERT’s
masked language modeling goal, but tailored for voice.

The feature encoder’s function is to minimize the dimensionality of audio data
by turning per 20 milliseconds the raw waveform into a sequence of feature
vectors $Z_{0},Z_{1},Z_{2},...,Z_{T}$. Its structure is straightforward: a
single-dimensional 7-layer convolutional neural network with 512 channels at
each layer. The waveform is adjusted before being delivered to the network,
and as we progress higher in the network, the kernel width and strides of the
convolutional layers decrease. The feature encoder has a total receptive field
of 400 samples, which is equivalent to 25 milliseconds of audio (audio data is
encoded at a sample rate of 16 kHz).

The continuous pattern of speech is one of the most significant challenges in
employing Transformers for speech processing. Written language may readily be
broken down into words or sub-words, resulting in a finite set of discrete
units. There are no natural sub-units in speech. Wav2vec 2.0 suggests sampling
from the Gumbel-Softmax distribution to learn discrete voice units
automatically. Codewords from codebooks are used to create possible units
(groups). The final speech unit is formed by concatenating codewords. Wav2vec
employs two groups, each of which has 320 potential words, for a total of 320
× 320 = 102,400 speech units. The logits are calculated by multiplying the
latent features by the quantization matrix: one score for each of the
available codewords in each codebook. After turning these logits into
probabilities, the Gumbel-Softmax approach allows sampling a single codeword
from each codebook.

The Transformer encoder, which takes the latent feature vectors as input and
processes them via 12 Transformer blocks for the BASE version of the model and
24 blocks for the LARGE version, is at the heart of wav2vec 2.0. To match the
Transformer encoder’s inner dimension, the input sequence must first pass
through a feature projection layer to raise the dimension from 512 (the CNN
output) to 768 for BASE or 1024 for LARGE.

The way positional information is supplied to the input differs from the
original Transformer design. In the initial approach, fixed pre-generated
positional embeddings were added to the input vectors since the Transformer’s
self-attention operation did not retain the order of the input sequence.
Instead, the wav2vec model learns relative positional embeddings on its own
using a new grouped convolution layer.

To train on unlabeled voice data, the pre-training method employs a
contrastive task. In the latent space, a mask is initially applied at random,
covering 50% of the projected latent feature vectors. Before being supplied to
the Transformer network, masked locations are replaced by the same learned
vector $Z^{\prime}_{M}$. The resulting context vectors are then projected to
match the dimension of the quantized speech units $Q_{t}$ in the final
projection layer. 100 negative distractors are evenly sampled from different
locations in the same text for each masked position. After that, the model
analyzes the similarity (cosine similarity) of the projected context vector
$C^{\prime}_{t}$ to the genuine positive target $Q_{p}$, as well as all
negative distractors $Q_{n}$. The contrastive loss thus rewards strong
similarity with the real positive target while punishing it with negative
distractors.

$\mathcal{L}_{m}=-log(\frac{exp(sim(c_{t},q_{t})/k)}{\sum_{\tilde{q}\in
Q_{t}}exp(sim(c_{t},\tilde{q})/k)})$ (1)

Another loss is added to the contrastive loss during pre-training to encourage
the model to employ all codewords equally. This is accomplished by increasing
the entropy of the Gumbel-Softmax distribution, preventing the model from
selecting only a tiny subset of all accessible codebook entries.

$\mathcal{L}_{d}=\frac{1}{GV}\sum_{g=1}^{G}-H(\bar{p_{g}})=\frac{1}{GV}\sum_{g=1}^{G}\sum_{v=1}^{V}\bar{p_{g,v}}log(\bar{p_{g,v}})$
(2)

$\mathcal{L}=\mathcal{L}_{m}+\alpha\mathcal{L}_{d}$ (3)

We fine-tuned the Wav2Vec 2.0 using Common Voice’s Persian dataset[33].The
model was then fine-tuned using our own data.

##  4 Results

The result of the suggested approach is described in this
phttps://www.overleaf.com/project/61e12e623b0cbd595f2885d7art, along with a
comparison to earlier efforts.

###  4.1 Dataset

All experiments in this paper are performed on the Common Voice’s Persian
dataset[33] and our own dataset. Common Voice is an audio dataset made up of
voices in MP3 format. The collection contains 9283 hours of recorded time.
Demographic data such as age, sex, and accent are also included in the
dataset. There are 7335 verified hours in 60 languages in the collection. In
Persian, this dataset includes 1240 voices with more than 70 hours of speech.

It is important to mention that we were unable to train and test our model
using the Farsdat dataset since it is not publicly available.

###  4.2 Evaluation Criteria

We employed Phoneme Error Rate (PER) and Word Error Rate (WER) in this study
to make a good comparison with earlier research. The number of all phoneme
mistakes (phonemes that have been added, removed, or modified) divided by the
total number of phonemes is the PER. The WER is derived from the Levenshtein
distance, working at the word level instead of the phoneme level.

$PER=\frac{Insert+Delete+Substitution}{Lengthofthephonemesequence}$ (4)

$WER=\frac{Insert+Delete+Substitution}{Numberofwords}$ (5)

###  4.3 Implementation setup

Table.3 shows the results of our experiments and comparisons to previous
works. As can be seen, the state-of-the-art model, Wav2Vec 2.0, may
significantly reduce errors. In Table.4 the WER on our dataset and Common
Voice dataset has been shown.

Table 3: PER on the test set of our experiments and previous works[35]. Model | Dataset | PER
---|---|---
Mono-phone | Farsdat | 30.5
Tri-phone | Farsdat | 20.8
SGMM | Farsdat | 19.9
SGMM + MMI | Farsdat | 19.8
Karel-DNN | Farsdat | 19.8
Karel-DNN | Farsdat | 19.8
Karel-DNN | Farsdat | 19.8
Wav2Vec 2.0 XLSR Large | Common Voice | 3.1
Wav2Vec 2.0 XLSR Large | Our dataset | 6.4
Table 4: WER of our model with and without training on Common Voice dataset and our dataset  Datasets | WER
---|---
Common Voice | 10.3
Our dataset | 34.4
Common Voice + Our dataset | 8.6

##  5 Selection and Participation of Children

In data collection and testing our model, children have participated in our
work. Due to the limitations of pandemic, selecting a vast and comprehensive
group of children in the wanted age from all around the country was difficult
and we just could use the participation of kids who live in Tehran.

All of our color data has been collected using the participation of children.
In meaningless word, children have participated too.

In addition, after the completion of the model and our website, the model has
been tested using the children’s voices of one school.

## Acknowledgments

We’d like to thank Mohammad Javad Dousti at the University of Southern
California for his brilliant model enhancement recommendations.

## References

  * [1] Sungbok Lee, Alexandros Potamianos, and Shrikanth Narayanan.  Analysis of children’s speech: Duration, pitch and formants.  In Fifth European Conference on Speech Communication and Technology, 1997.
  * [2] Raymond D Kent.  Anatomical and neuromuscular maturation of the speech mechanism: Evidence from acoustic studies.  Journal of speech and hearing Research, 19(3):421–447, 1976.
  * [3] Prashanth Gurunath Shivakumar, Alexandros Potamianos, Sungbok Lee, and Shrikanth S Narayanan.  Improving speech recognition for children using acoustic adaptation and pronunciation modeling.  In WOCCI, pages 15–19, 2014.
  * [4] Matteo Gerosa, Diego Giuliani, Shrikanth Narayanan, and Alexandros Potamianos.  A review of asr technologies for children’s speech.  In Proceedings of the 2nd Workshop on Child, Computer and Interaction, pages 1–8, 2009.
  * [5] Martin Russell and Shona D’Arcy.  Challenges for computer recognition of children’s speech.  In Workshop on Speech and Language Technology in Education, 2007\.
  * [6] Annika Hämäläinen, Sara Candeias, Hyongsil Cho, Hugo Meinedo, Alberto Abad, Thomas Pellegrini, Michael Tjalve, Isabel Trancoso, and Miguel Sales Dias.  Correlating asr errors with developmental changes in speech production: A study of 3-10-year-old european portuguese children’s speech.  In Workshop on Child Computer Interaction-WOCCI 2014, pages pp–1, 2014.
  * [7] Alexandros Potamianos, Shrikanth Narayanan, and Sungbok Lee.  Automatic speech recognition for children.  In Fifth European Conference on Speech Communication and Technology, 1997.
  * [8] Daniel Elenius and Mats Blomberg.  Adaptation and normalization experiments in speech recognition for 4 to 8 year old children.  In Interspeech, pages 2749–2752, 2005.
  * [9] Stephanie McMillen, Linda Jarmulowicz, Michael M Mackay, and D Kimbrough Oller.  Rapid shift in naming efficiency on a rapid automatic naming task by young spanish-speaking english language learners.  Applied Psycholinguistics, 41(4):847–872, 2020.
  * [10] Susan E Gathercole, Catherine S Willis, Alan D Baddeley, and Hazel Emslie.  The children’s test of nonword repetition: A test of phonological working memory.  Memory, 2(2):103–127, 1994.
  * [11] Kevin J Lang, Alex H Waibel, and Geoffrey E Hinton.  A time-delay neural network architecture for isolated word recognition.  Neural networks, 3(1):23–43, 1990.
  * [12] Abdel-rahman Mohamed, George Dahl, Geoffrey Hinton, et al.  Deep belief networks for phone recognition.  In Nips workshop on deep learning for speech recognition and related applications, volume 1, page 39. Vancouver, Canada, 2009.
  * [13] Li Deng, Michael L Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, and Geoff Hinton.  Binary coding of speech spectrograms using a deep auto-encoder.  In Eleventh annual conference of the international speech communication association. Citeseer, 2010.
  * [14] Frank Seide, Gang Li, and Dong Yu.  Conversational speech transcription using context-dependent deep neural networks.  In Twelfth annual conference of the international speech communication association, 2011.
  * [15] George E Dahl, Dong Yu, Li Deng, and Alex Acero.  Large vocabulary continuous speech recognition with context-dependent dbn-hmms.  In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 4688–4691. IEEE, 2011.
  * [16] Alex Graves, Navdeep Jaitly, and Abdel-rahman Mohamed.  Hybrid speech recognition with deep bidirectional lstm.  In 2013 IEEE workshop on automatic speech recognition and understanding, pages 273–278. IEEE, 2013.
  * [17] Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton.  Speech recognition with deep recurrent neural networks.  In 2013 IEEE international conference on acoustics, speech and signal processing, pages 6645–6649. Ieee, 2013.
  * [18] George Saon, Hong-Kwang J Kuo, Steven Rennie, and Michael Picheny.  The ibm 2015 english conversational telephone speech recognition system.  arXiv preprint arXiv:1505.05899, 2015.
  * [19] Jason Li, Vitaly Lavrukhin, Boris Ginsburg, Ryan Leary, Oleksii Kuchaiev, Jonathan M Cohen, Huyen Nguyen, and Ravi Teja Gadde.  Jasper: An end-to-end convolutional neural acoustic model.  arXiv preprint arXiv:1904.03288, 2019.
  * [20] Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, and Michael Auli.  wav2vec 2.0: A framework for self-supervised learning of speech representations.  arXiv preprint arXiv:2006.11477, 2020.
  * [21] Sharmistha S Gray, Daniel Willett, Jianhua Lu, Joel Pinto, Paul Maergner, and Nathan Bodenstab.  Child automatic speech recognition for us english: child interaction with living-room-electronic-devices.  In WOCCI, pages 21–26, 2014.
  * [22] Shweta Ghai and Rohit Sinha.  Exploring the role of spectral smoothing in context of children’s speech recognition.  In Tenth Annual Conference of the International Speech Communication Association, 2009.
  * [23] Hank Liao, Golan Pundak, Olivier Siohan, Melissa Carroll, Noah Coccaro, Qi-Ming Jiang, Tara N Sainath, Andrew Senior, Françoise Beaufays, and Michiel Bacchiani.  Large vocabulary automatic speech recognition for children.  2015\.
  * [24] Mahmood Bijankhan, Javad Sheikhzadegan, and Mahmood R Roohani.  Farsdat-the speech database of farsi spoken language.  PROCCEDINGS AUSTRALIAN CONFERENCE ON SPEECH SCIENCE AND TECHNOLOGY, 1994\.
  * [25] F Almasganj, SA Seyedsalehi, M Bijankhan, H Sameti, and J Sheikhzadegan.  Shenava-1: Persian spontaneous continuous speech recognizer.  In Proc. Int. Conf. on Electrical Engineering, pages 101–106, 2001\.
  * [26] Hossein Sameti, Hadi Veisi, Mohammad Bahrani, Bagher Babaali, and Khosro Hosseinzadeh.  Nevisa, a persian continuous speech recognition system.  In Computer Society of Iran Computer Conference, pages 485–492. Springer, 2008.
  * [27] Hossein Sameti, Hadi Veisi, Mohammad Bahrani, Bagher Babaali, and Khosro Hosseinzadeh.  A large vocabulary continuous speech recognition system for persian language.  EURASIP Journal on Audio, Speech, and Music Processing, 2011(1):1–12, 2011.
  * [28] Saman Azadi Yazdi.  Deep learning for speech recognition.  Master’s thesis, Sharif University of Technology, 2013.
  * [29] Mohammad Daneshvar and Hadi Veisi.  Persian phoneme recognition using long short-term memory neural network.  In 2016 Eighth International Conference on Information and Knowledge Technology (IKT), pages 111–115. IEEE, 2016.
  * [30] BAGHER BABAALI.  A state-of-the-art and efficient framework for persianspeech recognition.  2016\.
  * [31] Daniel Povey, Arnab Ghoshal, Gilles Boulianne, Lukas Burget, Ondrej Glembek, Nagendra Goel, Mirko Hannemann, Petr Motlicek, Yanmin Qian, Petr Schwarz, et al.  The kaldi speech recognition toolkit.  In IEEE 2011 workshop on automatic speech recognition and understanding, number CONF. IEEE Signal Processing Society, 2011.
  * [32] Hadi Veisi and Armita Haji Mani.  Persian speech recognition using deep learning.  International Journal of Speech Technology, 23(4):893–905, 2020\.
  * [33] Rosana Ardila, Megan Branson, Kelly Davis, Michael Henretty, Michael Kohler, Josh Meyer, Reuben Morais, Lindsay Saunders, Francis M Tyers, and Gregor Weber.  Common voice: A massively-multilingual speech corpus.  arXiv preprint arXiv:1912.06670, 2019.
  * [34] Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur.  Librispeech: an asr corpus based on public domain audio books.  In 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), pages 5206–5210. IEEE, 2015.
  * [35] Bagher and BabaAli.  A state-of-the-art and efficient framework for persian speech reognition.[research].  Signal and Data Processing, 13(3), 2016.