Speech self-supervised learning has attracted much attention due to its promising
performance in multiple downstream tasks, and has become a new growth engine for …

With the development of continuous speech recognition technology, users have put forward
higher requirements in terms of speech recognition accuracy. Low-resource speech …

India has more than 15001 languages, with 30 of them spoken by more than one million
native speakers. Most of them are low-resource and could greatly benefit from speech and …

This paper proposes a multilingual acoustic modeling approach for Indian languages using
a Multitask Learning (MTL) framework. Language-specific phoneme recognition is explored …

Semi-supervised training and language adversarial transfer learning are two different
techniques to improve the Automatic Speech Recognition (ASR) performance in limited …

Background: In India, thousands of languages or dialects are in use. Most Indian dialects are
low asset dialects. A well-performing Automatic Speech Recognition (ASR) system for Indian …

Automatic speech recognition systems for low-resource languages typically have smaller
corpora on which the language model is trained. Decoding with such a language model …

It is a need of time to build an automatic speech recognition (ASR) system for low-resource
languages. India is a multilingual country with more than 5000 languages, and out of which …

The submissions made by the INDIGO team at Indian Institute of Technology Guwahati (IITG)
to the Interspeech-2021 multilingual and code-switching Automatic Speech Recognition …