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An Improved Endpoint Detection Algorithm Based on MFCC Cosine Value

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Abstract

Endpoint detection is one of the most important steps in speech recognition. In a high SNR environment, the algorithm based on short-time energy and zero rate could be used. But when the SNR is low, this method may not be accurate. Some researchers proposed an algorithm which is based on MFCC Euclidean distance. It has a better performance in a noise environment. But that algorithm needs two thresholds to find the start and end point. However, when the values of two thresholds are not suitable, the detected result could be extremely bad. In this paper, we proposed an improved algorithm which is based on MFCC cosine value. This method can reduce errors, since it only needs one single threshold. The benefit of this improved algorithm is that the result can surely contain the real voice component. According to the experiment data, this improved algorithm can improve the speech recognition rate by 10% even in noise environment (SNR = 0). Thus, it proved that this improved methods has better robustness.

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Acknowledgements

This paper is supported by the National Natural Science Foundation of China (41471303), training project for outstanding young teachers of North China University of Technology, Special Research Foundation of North China University of Technology (PXM2017_014212_000014), Beijing Natural Science Foundation (4162022), and advantage disciplinary projects of North China University of Technology.

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  1. College of Computer Science and Technology, North China University of Technology, Beijing, 100144, China

    Danyang Cao, Xue Gao & Lei Gao

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  1. Danyang Cao
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  2. Xue Gao
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Correspondence to Danyang Cao.

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Cao, D., Gao, X. & Gao, L. An Improved Endpoint Detection Algorithm Based on MFCC Cosine Value. Wireless Pers Commun 95, 2073–2090 (2017). https://doi.org/10.1007/s11277-017-3958-0

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