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.
Highlights
The recent advancement in Automatic Speech Recognition (ASR) in the past couple of years is commendable, surpassing even human perception
DeepSpeech architecture needs Graphics Processing Unit (GPU) resources to run the training in minimal time, GPU is selected as the hardware accelerator in Google Colaboratory (GC)
Even though GC has usage time limits while using GPU, checkpoints are saved at regular intervals, which are continued after the time limit is revoked
Summary
The recent advancement in Automatic Speech Recognition (ASR) in the past couple of years is commendable, surpassing even human perception. We investigate the use of open-source speech recognition toolkits to build a speech recognition model for the Tamil language This developed pre-trained model will provide an out-of-the-box support for transfer learning for keyword spotting, isolated word recognition, etc. We present a novel approach to build a pre-trained model using low resources and substantially assist in developing a massive speech corpus using semi-supervised learning. To our knowledge, this is the first attempt to use the Common Voice dataset and release a pre-trained ASR model for Tamil language.
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