Speaker-independent phone modeling based on speaker-dependent HMMs' composition and clustering

Abstract

This paper proposes a novel method for speaker-independent phone modeling based on the composition and clustering method (CCL) of speaker-dependent HMMs. In general, HMM phone models are trained by the Baum-Welch (B-W) algorithm. We, however, propose a speaker-independent phone modeling in which speaker-dependent (SD) HMMs are combined to form speaker-independent (SI) HMMs without parameter reestimation. Furthermore, by using this method, we investigate how different kinds of reference speakers influence the development of the SI models. The method is evaluated in Japanese phoneme and phrase recognition experiments. Results show that the performance of this method is similar to the conventional B-W algorithm's with great reduction of computational cost.