End-to-End Speech Recognition for Low Resource Language Sanskrit using Self-Supervised Learning

Abstract

We are presenting the work on building a speaker independent, continuous speech recognition system for Samskruta (also called Sanskrit) using self-supervised learning. We have used a Pre-trained model from the Vakyansh team where the model is trained using 10,000 Hrs of data with 23 Indic languages and Fine-tuned it using a data-set containing nearly 78 Hrs of Samskruta audio along with their transcription taken from Vaksancaya - Sanskrit Speech Corpus from IIT Bombay. Acoustic representations are learned in an end-to-end deep learning approach using the wav2vec2.0 architecture from Fairseq. On top of this acoustic model, a language model is used to increase the overall performance. Our system provides a word error rate (WER) of 5.1 % on test data and 2.4% on train data. Meanwhile we built a graphical user interface in the form of a web page using the Flask framework, which provides an interactive platform for the user to record audio and see the transcription in real-time. To the best of our knowledge, our approach using self-supervised learning, gives better performance compared to the state of the art methods.