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IEICE Transactions on Information and Systems 2008 E91-D(3):488-498; doi:10.1093/ietisy/e91-d.3.488
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Copyright © 2008 The Institute of Electronics, Information and Communication Engineers

Special Section on Robust Speech Processing in Realistic Environments -- Papers -- Feature Extraction

Canonicalization of Feature Parameters for Robust Speech Recognition Based on Distinctive Phonetic Feature (DPF) Vectors

Mohammad NURUL HUDA1, Muhammad GHULAM1, Takashi FUKUDA2, Kouichi KATSURADA1 and Tsuneo NITTA1

1 The authors are with the Graduate School of Engineering, Toyohashi University of Technology, Toyohashi-shi, 441–8580 Japan E-mail: huda{at}vox.tutkie.tut.ac.jp, 2 Presently, with Tokyo Research Laboratory, IBM Japan Ltd.


  Abstract

This paper describes a robust automatic speech recognition (ASR) system with less computation. Acoustic models of a hidden Markov model (HMM)-based classifier include various types of hidden factors such as speaker-specific characteristics, coarticulation, and an acoustic environment, etc. If there exists a canonicalization process that can recover the degraded margin of acoustic likelihoods between correct phonemes and other ones caused by hidden factors, the robustness of ASR systems can be improved. In this paper, we introduce a canonicalization method that is composed of multiple distinctive phonetic feature (DPF) extractors corresponding to each hidden factor canonicalization, and a DPF selector which selects an optimum DPF vector as an input of the HMM-based classifier. The proposed method resolves gender factors and speaker variability, and eliminates noise factors by applying the canonicalzation based on the DPF extractors and two-stage Wiener filtering. In the experiment on AURORA-2J, the proposed method provides higher word accuracy under clean training and significant improvement of word accuracy in low signal-to-noise ratio (SNR) under multi-condition training compared to a standard ASR system with mel frequency ceptral coeffient (MFCC) parameters. Moreover, the proposed method requires a reduced, two-fifth, Gaussian mixture components and less memory to achieve accurate ASR.

Key Words: automatic speech recognition, feature extraction, canonicalization, distinctive phonetic feature, hidden factor

Manuscript received July 4, 2007. Manuscript revised September 10, 2007.


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