Shift-invariant features with multiple factors for robust text-independent speaker identification

Qiang Wu, Ju Liu, Jiande Sun, Andrzej Cichocki

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a new shift-invariant feature extraction method based on tensor analysis for robust text-independent speaker Identification. Multiple factors including time, frequency, scale and phase are investigated to extract the essential features of speech signal. In order to explore the shift-invariant characteristic of spectral features, we propose a convolutive model to learn the spectral basis function in tensor structure. Nonnegative assumption is also imposed on the CANDECOMP model to ensure the sparsity and preserve robust features. Experimental results demonstrate that our proposed method can improve the recognition accuracy specifically in noise conditions.

Original languageEnglish
Pages (from-to)8937-8944
Number of pages8
JournalJournal of Computational Information Systems
Volume8
Issue number21
Publication statusPublished - 1 Nov 2012
Externally publishedYes

Keywords

  • Convolutive tensor basis function
  • Robust feature extraction
  • Sparse representation
  • Speaker identification

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