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End-to-End Speech Recognition of Tamil Language

Mohamed Hashim Changrampadi1,*, A. Shahina2, M. Badri Narayanan2, A. Nayeemulla Khan3

1 Department of Electronics and Communication Engineering, C.Abdul Hakeem College of Engineering & Technology, Melvisharam, 632509, India
2 Department of Information Technology, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, 603110, India
3 School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600127, India

* Corresponding Author: Mohamed Hashim Changrampadi. Email: email

Intelligent Automation & Soft Computing 2022, 32(2), 1309-1323. https://doi.org/10.32604/iasc.2022.022021

Abstract

Research in speech recognition is progressing with numerous state-of-the-art results in recent times. However, relatively fewer research is being carried out in Automatic Speech Recognition (ASR) for languages with low resources. We present a method to develop speech recognition model with minimal resources using Mozilla DeepSpeech architecture. We have utilized freely available online computational resources for training, enabling similar approaches to be carried out for research in a low-resourced languages in a financially constrained environments. We also present novel ways to build an efficient language model from publicly available web resources to improve accuracy in ASR. The proposed ASR model gives the best result of 24.7% Word Error Rate (WER), compared to 55% WER by Google speech-to-text. We have also demonstrated a semi-supervised development of speech corpus using our trained ASR model, indicating a cost effective approach of building large vocabulary corpus for low resource language. The trained Tamil ASR model and the training sets are released in public domain and are available on GitHub.

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Cite This Article

M. Hashim Changrampadi, A. Shahina, M. Badri Narayanan and A. Nayeemulla Khan, "End-to-end speech recognition of tamil language," Intelligent Automation & Soft Computing, vol. 32, no.2, pp. 1309–1323, 2022. https://doi.org/10.32604/iasc.2022.022021



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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