Applying the Bi-level HMM for Robust Voice-activity Detection

Title
Applying the Bi-level HMM for Robust Voice-activity Detection
Authors
오상록Yongwon HwangMun-Ho JeongIl-Hwan Kim
Issue Date
2017-01
Publisher
JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY
Citation
VOL 12, NO 1-377
Abstract
This paper presents a voice-activity detection (VAD) method for sound sequences with various SNRs. For real-time VAD applications, it is inadequate to employ a post-processing for the removal of burst clippings from the VAD output decision. To tackle this problem, building on the bilevel hidden Markov model, for which a state layer is inserted into a typical hidden Markov model (HMM), we formulated a robust method for VAD not requiring any additional post-processing. In the method, a forward-inference-ratio test was devised to detect the speech endpoints and Mel-frequency cepstral coefficients (MFCC) were used as the features. Our experiment results show that, regarding different SNRs, the performance of the proposed approach is more outstanding than those of the conventional methods.
URI
http://pubs.kist.re.kr/handle/201004/69791
ISSN
1975-0102
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KIST Publication > Article
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