Using Spectral Fluctuation of Speech in multi-feature HMM-based voice activity detection

被引：0
|
作者：
Espi, Miquel [1 ]
Miyabe, Shigeki [1 ]
Nishimoto, Takuya [1 ]
Ono, Nobutaka [1 ]
Sagayama, Shigeki [1 ]
机构：
[1] Univ Tokyo, Grad Sch Informat Sci & Technol, Tokyo 1138654, Japan
来源：
12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5 | 2011年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Observation of speech spectrum leads to the fact that speech has a specific spectral fluctuation pattern both along time and frequency. In this paper, we integrate the usage of this nature in a multi-feature approach for voice activity detection. The effect of separating such specific spectral fluctuation using multi-stage HPSS (Harmonic-Percussive Sound Separation) has been analyzed over conventional features in voice activity detection, reducing frame-wise detection error by up to 78%, depending on the SNR conditions and noise type. The multi-feature approach has been tested using Hidden Markov Models to model the features stream as a sequence, which has out-performed standard and similar VAD proposals in utterance-based tests intended for automatic speech recognition.
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收藏
页码：2624 / 2627
页数：4
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