Applied Multi-Wavelet Feature to Text Independent Speaker Identification(Speech and Hearing)

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概要

• 論文の詳細を見る

• A new speaker feature extracted from multi-wavelet decomposition for speaker recognition is described. The multi-wavelet decomposition is a multi-scale representation of the covariance matrix. We have combined wavelet transform and the multi-resolution singular value algorithm to decompose eigenvector for speaker feature extraction not at the square matrix. Our results have shown that this multi-wavelet feature introduced better performance than the cepstrum and A-cepstrum with respect to the percentages of recognition.

• 社団法人電子情報通信学会の論文
• 2004-04-01

著者

• Lung S‐y

  Department Of Management Information Systems Chung-kuo Institute Of Technology

• Lung Shung-yung

  Department Of Management Information Systems Chung-kuo Institute Of Technology

関連論文

• Fuzzy Training Algorithm for Wavelet Codebook Based Text-Independent Speaker Identification(Speech and Hearing)
• Applied Multi-Wavelet Feature to Text Independent Speaker Identification(Speech and Hearing)

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