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Multiband Approach to Robust Text-Independent Speaker Identification

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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.

References


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鄭竹勝(2007)。以多階層向量量化為基礎之語者辨識〔碩士論文,淡江大學〕。華藝線上圖書館。https://doi.org/10.6846/TKU.2007.00755
林宗憲(2006)。應用於多頻段LC VCO之有系統的設計程序〔碩士論文,中原大學〕。華藝線上圖書館。https://doi.org/10.6840/cycu200600486

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