Auditory Sparse Representation for Robust Speaker Recognition Based on Tensor Structure. (18th November 2008)

• Record Type: Journal Article
• Authors: Wu, Qiang; Zhang, Liqing
• Other Names: Gan Woon-Seng Academic Editor.
• Is Part Of: EURASIP journal on audio, speech, and music processing. Volume 2008(2008)
• Publication Date: 2008-11-18
• 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: 10.1155/2008/578612
• Languages: English
• ISSNs: 1687-4714

Abstract

Abstract : This paper investigates the problem of speaker recognition in noisy conditions. A new approach called nonnegative tensor principal component analysis (NTPCA) with sparse constraint is proposed for speech feature extraction. We encode speech as a general higher-order tensor in order to extract discriminative features in spectrotemporal domain. Firstly, speech signals are represented by cochlear feature based on frequency selectivity characteristics at basilar membrane and inner hair cells; then, low-dimension sparse features are extracted by NTPCA for robust speaker modeling. The useful information of each subspace in the higher-order tensor can be preserved. Alternating projection algorithm is used to obtain a stable solution. Experimental results demonstrate that our method can increase the recognition accuracy specifically in noisy environments.