Recurrent Neural Networks and Acoustic Features for Frame-Level Signal-to-Noise Ratio Estimation

Abstract

It is important to know the presence and the relative level of background noise for many speech processing tasks. Frame-level signal-to-noise ratio (SNR) provides a measure of instantaneous noise level of a noisy signal, and its estimation has been researched for decades. This problem can be approached from a supervised learning perspective by predicting SNR from features of noisy speech. In this study, we introduce a deep learning algorithm for frame-level SNR estimation. The proposed algorithm employs recurrent neural networks (RNNs) with long short-term memory (LSTM) to leverage contextual information. We also systematically examine a range of acoustic features and investigate feature combinations using Group Lasso and sequential floating forward selection (SFFS). The proposed algorithm naturally leads to an utterance-level SNR estimator. Systematical evaluations show that the proposed algorithm provides an accurate estimate of frame-level SNR, as well as utterance-level SNR, under different noise conditions, outperforming other estimators.