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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 nonspeech segments. Based on this finding, we use the HSELT measures to achieve high accuracy for detecting speech signal form various stationary and nonstationary noises. key words: voice activity detection, horizontal spectral entropy, long-term, Mel-scaled filter bank

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