A speech feature extraction method using complexity measure for voice activity detection in WGN

Abstract

A novel speech extraction algorithm is proposed in this paper for Voice Activity Detection (VAD). Signal complexity analysis with definition of Kolmogorov complexity is adopted, which explores model characteristics of speech production to differentiate speech and white Gaussian noise (WGN). In the view of speech signal processing, properties of speech’s source and vocal tract are explored by complexity analysis. Also, some interesting properties of signal complexity are presented with experimental study, including complexity analysis of general noise-corrupted signal. Moreover, some enhanced features with complexity and a feature incorporation method are presented. These features incorporate some unique characteristics of speech, like pitch information, vocal organ information, and so on. With a large database of speech signals and synthetic/real Gaussian noise, distributions of novel features and receiver operating characteristics (ROC) curves are shown, which are proved as potential features for voice activity detection.