Speech Recognition using FHMMS Robust Against Nonstationary Noise
Agnieszka Betkowska, Koichi Shinoda, Sadaoki Furui · 2007
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.