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Sparse Representation with Optimized Learned Dictionary for Robust Voice Activity Detection

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Abstract

Traditionally, most of voice activity detection (VAD) methods are based on speech features such as spectrum, temporal energy, and periodicity. The robustness of these features plays a critical role on the performance of VAD. However, since these features are always directly generated from observed signal, the robustness of these features would be significantly degraded in non-stationary noise environments, especially at low level signal-to-noise ratio (SNR) condition. This paper proposes a kind of robust feature for VAD based on sparse representation with an optimized learned dictionary. To do so, a speech dictionary and a noise dictionary are first learned from speech corpus and noise corpus, respectively. Then an optimization algorithm is designed to reduce the mutual coherence between the two learned dictionaries. After that the proposed feature is generated from the optimized dictionary-based sparse representation, and a VAD method is derived from the proposed feature. The proposed method is evaluated over seven types of noise and four types of SNR level, experimental results show that the optimized dictionary is important for enhancing the robustness of the proposed method, and the proposed method performs well under non-stationary noise, especially at low level SNR condition.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant No. 91120303, No. 91220301, and No. 61170243, and the Ph. D Programs Foundation of Ministry of Education of China (No. 20112302110042).

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, Nan Gang District, Harbin , 150001, China

    Datao You, Jiqing Han, Guibin Zheng & Tieran Zheng

  2. Henan University, Minglun Street, Kaifeng , 475001, China

    Datao You & Jie Li

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  1. Datao You
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Correspondence to Datao You.

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You, D., Han, J., Zheng, G. et al. Sparse Representation with Optimized Learned Dictionary for Robust Voice Activity Detection. Circuits Syst Signal Process 33, 2267–2291 (2014). https://doi.org/10.1007/s00034-014-9748-y

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