37‐2: Invited Paper: Enhancing Speech in Noisy and Reverberant Environments Using Deep Learning Techniques

Abstract

Sound signals play a crucial role in immersive perceptual experiences, such as virtual and augmented reality applications and hearing assistant devices. Traditional approaches enhance speech by estimating the background noise or the speech based on its statistics or a parametric model. However, the performance of such an approach has plateaued due to mismatches between its assumptions and actual background noise and speech. Recently, deep learning (DL) has been applied to solve such a challenging problem by taking advantage of its ability to learn a nonlinear mapping and to recognize a pattern without making explicit assumptions about the background noise or speech. In this paper, we will provide a systematic review of single-microphone DL-based speech enhancement approaches. Through an analysis of their advantages and disadvantages, we will provide some insight into future research directions for speech enhancement for hearing devices. Author KeywordsSupervised speech enhancement; denoising; dereverberation; ideal ratio mask; speech intelligibility; long short-term memory; recurrent neural networks; deep neural networks.