Abstract
In this paper, we present a robust voice activity detection (VAD) algorithm using the combination of short-term and long-term spectral patterns. We analyze the benefit of short-term and long-term spectral patterns, respectively, when applied to robust VAD. Based on the analysis, we find the combination of short-term and long-term spectral patterns can be used to achieve a higher VAD accuracy than one of them only in noisy environments. We evaluate its performance under four types of noises and six types of signal-to-noise ratio (SNR) conditions. Compared with standard VAD schemes, the evaluation almost demonstrates promising results with the proposed scheme being comparable or favorable over the whole test set for various criterions of the VAD evaluation.
This research was supported in part by the China National Nature Science Foundation (No.91120303, No.61273267, No.90820011 and No.90820303).
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Tan, YW., Liu, WJ. (2014). Robust Voice Activity Detection Using the Combination of Short-Term and Long-Term Spectral Patterns. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45643-9_45
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DOI: https://doi.org/10.1007/978-3-662-45643-9_45
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