Speech Recognition using FHMMS Robust Against Nonstationary Noise

Abstract

We focus on the problem of speech recognition in the presence of nonstationary sudden noise, which is very likely to happen in home environments. As a model compensation method for this problem, we investigated the use of factorial hidden Markov model (FHMM) architecture developed from a clean-speech hidden Markov model (HMM) and a sudden-noise HMM. While in conventional studies this architecture is defined only for static features of the observation vector, we extended it to dynamic features. A database recorded by a personal robot called PaPeRo in home environments was used for the evaluation of the proposed method under noisy conditions. While we presented a recognition system using isolated-word FHMMs in our previous work, here we evaluated the effectiveness of the phoneme FHMMs.