Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process

Dunya Yousufzai

doi:doi:10.11648/j.ajnna.20210701.13

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Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process

Dunya Yousufzai

Published in American Journal of Neural Networks and Applications (Volume 7, Issue 1)

Received: 22 April 2021 Accepted: 17 May 2021 Published: 31 May 2021

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Abstract

There has been a considerable stream in ASR over the past few decades, but it may seem strange why this field is still a subject for researchers to work on. There are many reasons, but somewhat because the discipline is created with the promise of human-level performance under pragmatic states and this is an inextricable problem. In addition, the increasing advancement of technology in various fields has caused a more compelling need for this field. Especially the establishment of such a system in the security sector in insecure third world countries such as Afghanistan is an urgent need. This paper began with the reflection of all the necessary knowledge about speech recognition and then suggested an unprecedented method for building an automated speech recognition (ASR) system in the Dari language using the two most powerful open source engines CMUSphinx, from Carnegie Mellon University and DeepSpeech v0.9.3 /. These systems are much more impressive than early speech recognition systems. Using my own collected dataset, a speech-to-text model has been trained for the Dari language. Firstly, the dataset is filtered according to the task, then demonstrated the possible compatibility from the hidden Markov (HMM) models, the phoneme concept to RNN training. The system surpassed previously predicted results, as CMUSphinx stated, “for a typical 10-hour operation, the WER should be around 10%." Finally, 3.3% WER was achieved with 10.3-hours of audio recording using CMUSphinx. 1% WER with DeepSpeech.

Published in	American Journal of Neural Networks and Applications (Volume 7, Issue 1)
DOI	10.11648/j.ajnna.20210701.13
Page(s)	15-22
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Dari Language, HMM (S), Neural Network, Non-speaking English Countries, RNN, Speech-recognition, WER

References

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[42]	The credit for the first figure goes to Rolphe Frédéric Fehlmann, it is designed by Dunya Yousufzai but the concept was taken from Rolphe Frédéric Fehlmann.

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APA Style

Dunya Yousufzai. (2021). Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process. American Journal of Neural Networks and Applications, 7(1), 15-22. https://doi.org/10.11648/j.ajnna.20210701.13

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ACS Style

Dunya Yousufzai. Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process. Am. J. Neural Netw. Appl. 2021, 7(1), 15-22. doi: 10.11648/j.ajnna.20210701.13

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AMA Style

Dunya Yousufzai. Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process. Am J Neural Netw Appl. 2021;7(1):15-22. doi: 10.11648/j.ajnna.20210701.13

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@article{10.11648/j.ajnna.20210701.13,
  author = {Dunya Yousufzai},
  title = {Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process},
  journal = {American Journal of Neural Networks and Applications},
  volume = {7},
  number = {1},
  pages = {15-22},
  doi = {10.11648/j.ajnna.20210701.13},
  url = {https://doi.org/10.11648/j.ajnna.20210701.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20210701.13},
  abstract = {There has been a considerable stream in ASR over the past few decades, but it may seem strange why this field is still a subject for researchers to work on. There are many reasons, but somewhat because the discipline is created with the promise of human-level performance under pragmatic states and this is an inextricable problem. In addition, the increasing advancement of technology in various fields has caused a more compelling need for this field. Especially the establishment of such a system in the security sector in insecure third world countries such as Afghanistan is an urgent need. This paper began with the reflection of all the necessary knowledge about speech recognition and then suggested an unprecedented method for building an automated speech recognition (ASR) system in the Dari language using the two most powerful open source engines CMUSphinx, from Carnegie Mellon University and DeepSpeech v0.9.3 /. These systems are much more impressive than early speech recognition systems. Using my own collected dataset, a speech-to-text model has been trained for the Dari language. Firstly, the dataset is filtered according to the task, then demonstrated the possible compatibility from the hidden Markov (HMM) models, the phoneme concept to RNN training. The system surpassed previously predicted results, as CMUSphinx stated, “for a typical 10-hour operation, the WER should be around 10%." Finally, 3.3% WER was achieved with 10.3-hours of audio recording using CMUSphinx. 1% WER with DeepSpeech.},
 year = {2021}
}

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TY  - JOUR
T1  - Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process
AU  - Dunya Yousufzai
Y1  - 2021/05/31
PY  - 2021
N1  - https://doi.org/10.11648/j.ajnna.20210701.13
DO  - 10.11648/j.ajnna.20210701.13
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 15
EP  - 22
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20210701.13
AB  - There has been a considerable stream in ASR over the past few decades, but it may seem strange why this field is still a subject for researchers to work on. There are many reasons, but somewhat because the discipline is created with the promise of human-level performance under pragmatic states and this is an inextricable problem. In addition, the increasing advancement of technology in various fields has caused a more compelling need for this field. Especially the establishment of such a system in the security sector in insecure third world countries such as Afghanistan is an urgent need. This paper began with the reflection of all the necessary knowledge about speech recognition and then suggested an unprecedented method for building an automated speech recognition (ASR) system in the Dari language using the two most powerful open source engines CMUSphinx, from Carnegie Mellon University and DeepSpeech v0.9.3 /. These systems are much more impressive than early speech recognition systems. Using my own collected dataset, a speech-to-text model has been trained for the Dari language. Firstly, the dataset is filtered according to the task, then demonstrated the possible compatibility from the hidden Markov (HMM) models, the phoneme concept to RNN training. The system surpassed previously predicted results, as CMUSphinx stated, “for a typical 10-hour operation, the WER should be around 10%." Finally, 3.3% WER was achieved with 10.3-hours of audio recording using CMUSphinx. 1% WER with DeepSpeech.
VL  - 7
IS  - 1
ER  -

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Author Information

Dunya Yousufzai

Computer Science, Software Engineering Department, Kabul University, Kabul, Afghanistan

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APA Style

Dunya Yousufzai. (2021). Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process. American Journal of Neural Networks and Applications, 7(1), 15-22. https://doi.org/10.11648/j.ajnna.20210701.13

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ACS Style

Dunya Yousufzai. Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process. Am. J. Neural Netw. Appl. 2021, 7(1), 15-22. doi: 10.11648/j.ajnna.20210701.13

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AMA Style

Dunya Yousufzai. Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process. Am J Neural Netw Appl. 2021;7(1):15-22. doi: 10.11648/j.ajnna.20210701.13

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@article{10.11648/j.ajnna.20210701.13,
  author = {Dunya Yousufzai},
  title = {Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process},
  journal = {American Journal of Neural Networks and Applications},
  volume = {7},
  number = {1},
  pages = {15-22},
  doi = {10.11648/j.ajnna.20210701.13},
  url = {https://doi.org/10.11648/j.ajnna.20210701.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20210701.13},
  abstract = {There has been a considerable stream in ASR over the past few decades, but it may seem strange why this field is still a subject for researchers to work on. There are many reasons, but somewhat because the discipline is created with the promise of human-level performance under pragmatic states and this is an inextricable problem. In addition, the increasing advancement of technology in various fields has caused a more compelling need for this field. Especially the establishment of such a system in the security sector in insecure third world countries such as Afghanistan is an urgent need. This paper began with the reflection of all the necessary knowledge about speech recognition and then suggested an unprecedented method for building an automated speech recognition (ASR) system in the Dari language using the two most powerful open source engines CMUSphinx, from Carnegie Mellon University and DeepSpeech v0.9.3 /. These systems are much more impressive than early speech recognition systems. Using my own collected dataset, a speech-to-text model has been trained for the Dari language. Firstly, the dataset is filtered according to the task, then demonstrated the possible compatibility from the hidden Markov (HMM) models, the phoneme concept to RNN training. The system surpassed previously predicted results, as CMUSphinx stated, “for a typical 10-hour operation, the WER should be around 10%." Finally, 3.3% WER was achieved with 10.3-hours of audio recording using CMUSphinx. 1% WER with DeepSpeech.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Early Stages of Automatic Speech Recognition (ASR) in Non-english Speaking Countries and Factors That Affect the Recognition Process
AU  - Dunya Yousufzai
Y1  - 2021/05/31
PY  - 2021
N1  - https://doi.org/10.11648/j.ajnna.20210701.13
DO  - 10.11648/j.ajnna.20210701.13
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 15
EP  - 22
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20210701.13
AB  - There has been a considerable stream in ASR over the past few decades, but it may seem strange why this field is still a subject for researchers to work on. There are many reasons, but somewhat because the discipline is created with the promise of human-level performance under pragmatic states and this is an inextricable problem. In addition, the increasing advancement of technology in various fields has caused a more compelling need for this field. Especially the establishment of such a system in the security sector in insecure third world countries such as Afghanistan is an urgent need. This paper began with the reflection of all the necessary knowledge about speech recognition and then suggested an unprecedented method for building an automated speech recognition (ASR) system in the Dari language using the two most powerful open source engines CMUSphinx, from Carnegie Mellon University and DeepSpeech v0.9.3 /. These systems are much more impressive than early speech recognition systems. Using my own collected dataset, a speech-to-text model has been trained for the Dari language. Firstly, the dataset is filtered according to the task, then demonstrated the possible compatibility from the hidden Markov (HMM) models, the phoneme concept to RNN training. The system surpassed previously predicted results, as CMUSphinx stated, “for a typical 10-hour operation, the WER should be around 10%." Finally, 3.3% WER was achieved with 10.3-hours of audio recording using CMUSphinx. 1% WER with DeepSpeech.
VL  - 7
IS  - 1
ER  -

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