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篇名
Multiband Approach to Robust Text-Independent Speaker Identification
作者 Chen, Wan-chen (Chen, Wan-chen)Hsieh,Ching-tang (Hsieh,Ching-tang)Lai, Eugene (Lai, Eugene)
中文摘要
This paper presents an effective method for improving the performance of a speaker identification system. Based on the multiresolution property of the wavelet transform, the input speech signal is decomposed into various frequency bands in order not to spread noise distortions over the entire feature space. To capture the characteristics of the vocal tract, the linear predictive cepstral coefficients (LPCCs) of each band are calculated. Furthermore, the cepstral mean normalization technique is applied to all computed features in order to provide similar parameter statistics in all acoustic environments. In order to effectively utilize these multiband speech features, we use feature recombination and likelihood recombination methods to evaluate the task of text-independent speaker identification. The feature recombination scheme combines the cepstral coefficients of each band to form a single feature vector used to train the Gaussian mixture model (GMM). The likelihood recombination scheme combines the likelihood scores of the independent GMM for each band. Experimental results show that both proposed methods achieve better performance than GMM using full-band LPCCs and mel-frequency cepstral coefficients (MFCCs) when the speaker identification is evaluated in the presence of clean and noisy environments.
起訖頁 63-75
關鍵詞 Speaker identificationWavelet transformLinear predictive cepstral coefficientLPCCMel-frequency cepstral coefficientMFCCGaussian mixture modelGMM
刊名 中文計算語言學期刊  
期數 200408 (9:2期)
出版單位 中華民國計算語言學學會
該期刊-上一篇 Multi-Modal Emotion Recognition from Speech and Text
該期刊-下一篇 An Innovative Distributed Speech Recognition Platform for Portable, Personalized and Humanized Wireless Devices
 

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