Studies on pattern recognition approach to voiced-unvoiced-silence classification

A pattern recognition approach for deciding whether a given segment of speech should be classified as voiced speech, unvoiced speech or silence based on a set of five measurements of the signal is given by Atal and Rabiner [1]. In this paper, we demonstrate that it is possible to achieve this classification with much less computational effort. These computational savings are mainly achieved by adopting a scheme based on the concept of variable decision space, using only three features and by avoiding the time consuming linear prediction analysis.