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KMID : 1022420130050010011
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Phonetics and Speech Sciences
2013 Volume.5 No. 1 p.11 ~ p.16
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Robust Feature Extraction for Voice Activity Detection in Nonstationary Noisy Environments
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Hong Jung-Pyo
Park Sang-Joon
Jeong Sang-Bae
Han Min-Soo
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Abstract
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This paper proposes robust feature extraction for accurate voice activity detection (VAD). VAD is one of the principal modules for speech signal processing such as speech codec, speech enhancement, and speech recognition. Noisy environments contain nonstationary noises causing the accuracy of the VAD to drastically decline because the fluctuation of features in the noise intervals results in increased false alarm rates. In this paper, in order to improve the VAD performance, harmonic-weighted energy is proposed. This feature extraction method focuses on voiced speech intervals and weighted harmonic-to-noise ratios to determine the amount of the harmonicity to frame energy. For performance evaluation, the receiver operating characteristic curves and equal error rate are measured.
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KEYWORD
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voice activity detection, robust feature extraction, harmonic-to-noise ratio, harmonic-weighted energy홍정표
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