Integrated phoneme subspace method for speech feature extraction

Authors:
Hyunsin Park;Tetsuya Takiguchi;Yasuo Ariki
Affiliations:
Graduate School of Engineering, Kobe University, Kobe, Japan;Graduate School of Engineering, Kobe University, Kobe, Japan;Graduate School of Engineering, Kobe University, Kobe, Japan
Venue:
EURASIP Journal on Audio, Speech, and Music Processing
Year:
2009

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Abstract

Speech feature extraction has been a key focus in robust speech recognition research. In this work, we discuss data-driven linear feature transformations applied to feature vectors in the logarithmic mel-frequency filter bank domain. Transformations are based on principal component analysis (PCA), independent component analysis (ICA), and linear discriminant analysis (LDA). Furthermore, this paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and reconstructs feature space for representing phonemic information efficiently. The proposed speech feature vector is generated by projecting an observed vector onto an integrated phoneme subspace (IPS) based on PCA or ICA. The performance of the new feature was evaluated for isolated word speech recognition. The proposed method provided higher recognition accuracy than conventional methods in clean and reverberant environments.