Canonicalization of feature parameters for automatic speech recognition

Acoustic models (AMs) of an HMM-based classifier include various types of hidden variables such as gender type, speaking rate, and acoustic environment. If there exists a canonicalization process that reduces the influence of the hidden variables from the AMs, a robust automatic speech recognition (ASR) system can be realized. In this paper, we describe the configuration of a canonicalization process targeting gender type as a hidden variable. The proposed canonicalization process is composed of multiple distinctive phonetic feature (DPF) extractors corresponding to the hidden variable and a DPF selector in which the distance between input DPF and AMs is compared. In a DPF extraction stage, an input sequence of acoustic feature vectors is mapped onto three DPF spaces corresponding to male, female, and neutral voice by using three multilayer neural networks (MLNs). Experiments are carried out by comparing (A) the combination of the canonicalized DPF and a single HMM classifier, and (B) the combination of a single acoustic feature (MFCC) and multiple HMM classifiers. The result shows that the proposed canonicalization method outperforms both of the conventional ASR with MFCC and a single HMM and the ASR with multiple HMMs in spite of less memories and computation time.