Advances in Low Resource ASR: A Deep Learning Perspective

Abstract

Recently, developing Automatic Speech Recognition (ASR) systems for Low Resource (LR) languages is an active research area. The research in ASR is significantly advanced using deep learning approaches producing state-of-the-art results compared to the conventional approaches. However, it is still challenging to use such approaches for LR languages since it requires a huge amount of training data. Recently, data augmentation, multilingual and cross-lingual approaches, transfer learning, etc. enable training deep learning architectures. This paper presents an overview of deep learning-based approaches for building ASR for LR languages. Recent projects and events organized to support the development of ASR and related applications in this direction are also discussed. This paper could be a good motivation for the researchers interested to work towards low resource ASR using deep learning techniques. The approaches described here could be useful in other related applications, such as audio search.