Integrated Phoneme Subspace Method for Speech Feature Extraction. (16th June 2009)

• Record Type: Journal Article
• Authors: Park, Hyunsin; Takiguchi, Tetsuya; Ariki, Yasuo
• Other Names: Milner Ben Academic Editor.
• Is Part Of: EURASIP journal on audio, speech, and music processing. Volume 2009(2009)
• Publication Date: 2009-06-16
• Subjects: Sound -- Recording and reproducing -- Digital techniques -- Periodicals; Computer sound processing -- Periodicals; Computer sound processing; Sound -- Recording and reproducing -- Digital techniques; Periodicals; Electronic journal; Electronic journals; 620.2
• DOI: 10.1155/2009/690451
• Languages: English
• ISSNs: 1687-4714

Abstract

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.