Voiced/unvoiced speech discrimination in noise using Gabor atomic decomposition

Abstract

A new algorithm is developed for voiced-unvoiced speech discrimination in noise. Short segments of speech are modeled as a sum of basis functions from a Gabor dictionary. In each iteration, a Gabor atom is fitted (using the matching pursuit algorithm) to the residual obtained by subtracting the best-fit Gabor atom from the previous residual. Multiple discriminant analysis is used to reduce the dimensionality of the vector of Gabor coefficients to give a low-dimensional feature vector for classification. A radial basis function neural network is trained on the reduced feature vector set to discriminate between voiced and unvoiced speech/silence segments. On a database of 62 sentences in 5-dB SNR speech-shaped noise, 84% correct classification accuracy was obtained.