Designing multiple distinctive phonetic feature extractors for canonicalization by using clustering technique

Acoustic models of an HMM-based classifier include various types of hidden factors such as speaker-specific characteristics and acoustic environments. If there exist a canonicalization process that represses the decrease of differences in acoustic-likelihood among categories resulted from hidden factors, a robust ASR system can be realized. We have previously proposed the canonicalization process of featureparameters composed of three distinctive phonetic feature (DPF) extractors focused on a gender factor. This paper describes an attempt to design multiple DPF extractors corresponding to unspecific hidden factors, as well as to introduce a noise suppressor that is targeted for the canonicalization of a noise factor. In an experiment on Japanese version AURORA2 database (AURORA2-J), the proposed system achieved significant improvements when combining the canonicalization process with the noise reduction technique based on a two-stage Wiener filter.