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Báo cáo hóa học: "Research Article Integrated Phoneme Subspace Method for Speech Feature Extraction"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Integrated Phoneme Subspace Method for Speech Feature Extraction | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2009 Article ID 690451 6 pages doi 10.1155 2009 690451 Research Article Integrated Phoneme Subspace Method for Speech Feature Extraction Hyunsin Park Tetsuya Takiguchi and Yasuo Ariki Graduate School of Engineering Kobe University 1-1 Rokkodai-cho Nada-ku Kobe 657-8501 Japan Correspondence should be addressed to Hyunsin Park silentbattle@gmail.com Received 31 July 2008 Revised 14 January 2009 Accepted 24 March 2009 Recommended by Ben Milner 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. Copyright 2009 Hyunsin Park et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. Introduction In the case of distant hands-free speech recognition system performance decreases sharply due to the effects of reverberation. To solve this problem there have been many studies carried out on feature extraction model adaptation and decoding. Our proposed method .