Classification of transition sounds with application to automatic speech recognition

Abstract

This paper addresses the problem of classification of speech transition sounds. A number of non parametric classifiers are compared, and it is shown that some non-parametric classifiers have considerable advantages over traditional hidden Markov models. Among the non-parametric classifiers, support vector machines were found the most suitable and the easiest to tune. Some of the reasons for the superiority of non-parametric classifiers will be discussed. The algorithm was tested on the voiced stop consonant phones extracted from the TIMIT corpus and resulted in very low error rates.