The Optimized Dictionary based Robust Speaker Recognition

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• 作者：Datao You ; Baojun Qiao ; Jie Li
• 关键词：Optimized dictionary ; Robust speaker recognition ; Sparse representation
• 刊名：Journal of Signal Processing Systems
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：86
• 期：2-3
• 页码：289-297
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing; Circuits and Systems; Electrical Engineering; Image Processing and Computer Vision; Pattern Recognition; Computer Imaging, Vision, Pattern Recognition and Graphics;
• 出版者：Springer US
• ISSN：1939-8115
• 卷排序：86

文摘

The mismatch between the training and the testing environments greatly degrades the performance of speaker recognition. Although many robust techniques have been proposed, the mismatch problem is still a challenge for speaker recognition system. To solve this problem, we propose an optimized dictionary based sparse representation for robust speaker recognition. To this end, we first train a speech dictionary and a noise dictionary, and concatenate them for sparse representation; then design an optimization algorithm to reduce the mutual coherence between the two learned dictionaries; after that, utilize mixture k-means to model speaker corresponding to sparse feature; and finally, present a distance divergence to measure the similarity. Compared with the Mel-frequency cepstral coefficients based speaker recognition, our preliminary experiments show that the proposed recognition framework consistently improve the robustness in the mismatched condition.