Abstract

This paper introduces a supervised statistical framework for estimating the signal-to-noise (SNR) ratio of speech signals. Information on how noise corrupts a signal can help us compensate for its effects, especially in real life applications where the usual assumption of white Gaussian noise does not hold and speech boundaries in the signal are not known. We use features from which we can detect speech regions in a signal, without using Voice Activity Detection, and estimate the energies of those regions. Then we use these features to train ordinary least squares regression models for various noise types. We compare this supervised method with state-of-the-art SNR estimation algorithms and show its superior performance with respect to the tested noise types.

Date

May 4, 2014

Authors

Pavlos Papadopoulos, Andreas Tsiartas, James Gibson, Shrikanth Narayanan

Conference

2014 IEEE International conference on acoustics, speech and signal processing (ICASSP)

Pages

8237-8241

Publisher

IEEE