Text-independent speaker identification system based on adaptive wavelets

Abstract

In this paper, we describe a text independent, phoneme based speaker identification system which uses adaptive wavelets to model the phonemes. This system identifies a speaker by modeling a very short segment of phonemes and then by clustering all the phonemes belonging to the same speaker into one class. The classification is achieved by using a two layer feed forward neural network classifier. The performance of this speaker identification system is demonstrated by considering the phonemes that were extracted from various sentences spoken by three speakers in the TIMIT acoustic-phonetic speech corpus.