Abstract
Endpoint detection plays a crucial role in speech recognition systems. An effective endpoint detection algorithm can not only reduce the processing time, but also can interfere with the noise of the silent segment. The traditional endpoint detection algorithms are mostly processed in a noise-free environment, so there will be problems such as weak noise immunity. In the problem of low SNR, this paper proposes an improved endpoint detection algorithm based on improved spectral subtraction with multi-taper spectrum and energy-zero ratio. The algorithm uses the improved spectral subtraction method of multi-window spectrum estimation to reduce the speech noise, and then combines the energy-zero ratio with endpoint detection. Experiments show that the proposed algorithm has better robustness under different SNR conditions.
This paper was supported by National Undergraduate Innovation Project with Granted No. 201710488004 and Fund of Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System with Granted No. znxx2018MS03 and znxx2018QN07.
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Bao, T., Li, Y., Xu, K., Wang, Y., Hu, W. (2018). An Improved Endpoint Detection Algorithm Based on Improved Spectral Subtraction with Multi-taper Spectrum and Energy-Zero Ratio. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_25
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