Horizontal Spectral Entropy with Long-Span of Time for Robust Voice Activity Detection

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概要

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• This letter introduces innovative VAD based on horizontal spectral entropy with long-span of time (HSELT) feature sets to improve mobile ASR performance in low signal-to-noise ratio (SNR) conditions. Since the signal characteristics of nonstationary noise change with time, we need long-term information of the noisy speech signal to define a more robust decision rule yielding high accuracy. We find that HSELT measures can horizontally enhance the transition between speech and non-speech segments. Based on this finding, we use the HSELT measures to achieve high accuracy for detecting speech signal form various stationary and nonstationary noises.

著者

• WANG Kun-Ching

  Shih Chien University

関連論文

• Horizontal Spectral Entropy with Long-Span of Time for Robust Voice Activity Detection

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