Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

TL;DR

This paper reviews recent deep learning methods designed to improve speech recognition robustness in challenging environmental noise conditions, highlighting advances in single- and multi-channel approaches and joint training frameworks.

Contribution

It provides a comprehensive overview of recent deep learning techniques for environmentally robust speech recognition, offering guidelines for future development.

Findings

Deep learning approaches effectively mitigate non-stationary noise effects.

Multi-channel techniques enhance speech recognition accuracy in noisy environments.

Joint front-end and back-end training frameworks improve robustness.

Abstract

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks.