We focus on the problem of speech recognition in the presence of nonstationary sudden noise, which is very likely to happen in home environments. To handle this problem, a model compensation method based on a factorial hidden Markov model (FHMM) has been recently introduced. In this architecture, speech and noise processes are modeled in parallel by a phoneme FHMM that is built by combining a clean-speech phoneme hidden Markov model (HMM) and a sudden noise HMM. Here, to increase the robustness of this method further, we apply supervised and unsupervised home-environment adaptation of phoneme FHMMs. A database recorded by a personal robot PaPeRo in home environments was used for the evaluation of the proposed method under noisy conditions. The phoneme home-dependent FHMM achieved better recognition accuracy than the clean-speech home-independent HMM, reducing the overall relative error by 16.2% and 12.3% on average for supervised and unsupervised adaptation, respectively.
['Agnieszka Betkowska', 'Koichi Shinoda', 'Sadaoki Furui']
Home-environment adaptation of phoneme factorial hidden Markov models