Separation of Fricatives and Affricates

Abstract

Separating speech from acoustic interference is a very challenging task. In particular, no system successfully addresses the separation of unvoiced speech. Fricatives and affricates are two main categories of consonants that contain a significant amount of unvoiced signal. We propose a novel system that separates fricatives and affricates from non-speech interference. The system first decomposes the input mixture into segments, each of which contains signal mainly from one source. Then it detects segments dominated by unvoiced portions of fricatives and affricates with a feature-based Bayesian classifier, and groups these segments with voiced speech separated by a previous system. The proposed system is evaluated with various types of interference and produces promising results.