Change Point Detection in GARCH Models for Voice Activity Detection

Abstract

This paper presents a robust algorithm for voice activity detection (VAD) based on change point detection in a generalized autoregressive conditional heteroscedasticity (GARCH) process. GARCH models are new statistical methods that are used especially in economic time series and are a popular choice to model speech signals and their changing variances. Change point detection is also important in economic sciences. In this paper, no distinct probability functions are assumed for speech and noise distributions. Also, to detect speech/nonspeech intervals, no likelihood ratio test (LRT) is employed. For testing parameter constancy in GARCH models, the algorithm of the Cramer-von Mises (CVM) test is described. This test is a nonparametric test and is based on the empirical quantiles. We show that VAD is related to the parameter constancy test in GARCH process, and we illustrate several examples.