Speaker recognition using HMM composition in noisy environments
Tomoko Matsui, Tomohito Kanno, Sadaoki Furui · 1995
Abstract This paper investigates a speaker recognition method that is robust against background noise. In noisy environments, one important issue is how to create a model for each speaker so as to compensate for noise. The method described here is based on hidden Markov model (HMM) composition, which combines a speaker HMM and a noise-source HMM into a noise-added speaker HMM with a particular signal-to-noise ratio (SNR). Since it is difficult to measure the SNR of input speech with non-stationary noise exactly, this method creates several noise-added speaker HMMs with various SNRs. The HMM that has the highest likelihood value for the input speech is selected, and a speaker decision is made using this likelihood value. Experimental application of this method to text-independent speaker identification and verification in various kinds of noisy environments demonstrated considerable improvement in speaker recognition for speech utterances of male speakers.