Recent Advances in Robust Speech Recognition Technology

by

Javier Ramírez, Juan Manuel Górriz

DOI: 10.2174/9781608051724111010
eISBN: 978-1-60805-172-4, 2011
ISBN: 978-1-60805-389-6



Indexed in: Scopus, EBSCO.

This E-book is a collection of articles that describe advances in speech recognition technology. Robustness in speech recognition refe...[view complete introduction]
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Using GARCH Process for Voice Activity Detection

- Pp. 13-29 (17)

Rasool Tahmasbi

Abstract

GARCH (Generalized Autoregressive Conditional Heteroscedasticity) models are new statistical methods that are used especially in economic time series. There is a consensus that speech signals exhibit variance that changes through time. GARCH models are a popular choice to model these changing variances. In this chapter, we propose three methods for VAD, which are based on GARCH models. In the first method, heteroscedasticity will be modeled by GARCH process and hard detection is the result of comparing a Multiple Observation Likelihood Ratio Test (MOLRT) with a threshold function. In the second method, no distinct probability functions are assumed for speech and noise distributions and no LRT is employed. We will show that VAD is related to the parameter constancy test in GARCH process. For testing parameter constancy in GARCH models, the algorithm of the Cramer-von Mises (CVM) test is described. In the last method, the process of outlier detection in GARCH model is presented and the motivation for using it and its relation with VAD are discussed.

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