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The Optimized Dictionary based Robust Speaker Recognition

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Abstract

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.

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Authors and Affiliations

  1. Software School, Henan University, Kaifeng, Henan, 475000, China

    Datao You, Baojun Qiao & Jie Li

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  1. Datao You
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  2. Baojun Qiao
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  3. Jie Li
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Corresponding author

Correspondence to Baojun Qiao.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant No. 61272544 and No. 61170243

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You, D., Qiao, B. & Li, J. The Optimized Dictionary based Robust Speaker Recognition. J Sign Process Syst 86, 289–297 (2017). https://doi.org/10.1007/s11265-016-1121-x

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  • DOI: https://doi.org/10.1007/s11265-016-1121-x

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