Noise Robust Speaker Identification Using Sub-Band Weighting in Multi-Band Approach(Speech and Hearing)

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概要

• 論文の詳細を見る

• Recently, many techniques have been proposed to improve speaker identification in noise environments. Among these techniques, we consider the feature recombination technique for the multi-band approach in noise robust speaker identification. The conventional feature recombination technique is very effective in the band-limited noise condition, but in broad-band noise condition, the conventional feature recombination technique does not provide notable performance improvement compared with the full-band system. Even though the speech is corrupted by the broad-band noise, the degree of the noise corruption on each sub-band is different from each other. In the conventional feature recombination for speaker identification, all sub-band features are used to compute multi-band likelihood score, but this likelihood computation does not use a merit of multi-band approach effectively, even though the sub-band features are extracted independently. Here we propose a new technique of sub-band likelihood computation with sub-band weighting in the feature recombination method. The signal to noise ratio (SNR) is used to compute the sub-band weights. The proposed sub-band-weighted likelihood computation makes a speaker identification system more robust to noise. Experimental results show that the average error reduction rate (ERR) in various noise environments is more than 24% compared with the conventional feature recombination-based speaker identification system.

• 社団法人電子情報通信学会の論文
• 2007-12-01

著者

• Kim Hoirin

  Korea Advanced Inst. Sci. And Technol. Daejeon Kor

• Kim Hoirin

  School Of Engineering At Information And Communications University

• KIM Sungtak

  School of Engineering at Information and Communications University

• JI Mikyong

  School of Engineering at Information and Communications University

• SUH Youngjoo

  School of Engineering at Information and Communications University

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• Soft Counting Poisson Mixture Model-Based Polling Method for Speech/Nonspeech Classification(Speech and Hearing)
• Noise Robust Speaker Identification Using Sub-Band Weighting in Multi-Band Approach

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