Formant-Based Robust Voice Activity Detection
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoo, In-Chul | - |
dc.contributor.author | Lim, Hyeontaek | - |
dc.contributor.author | Yook, Dongsuk | - |
dc.date.accessioned | 2021-09-04T10:05:53Z | - |
dc.date.available | 2021-09-04T10:05:53Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2015-12 | - |
dc.identifier.issn | 2329-9290 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/91746 | - |
dc.description.abstract | Voice activity detection (VAD) can be used to distinguish human speech from other sounds, and various applications can benefit from VAD-including speech coding and speech recognition. To accurately detect voice activity, the algorithm must take into account the characteristic features of human speech and/or background noise. In many real-life applications, noise frequently occurs in an unexpected manner, and in such situations, it is difficult to determine the characteristics of noise with sufficient accuracy. As a result, robust VAD algorithms that depend less on making correct noise estimates are desirable for real-life applications. Formants are the major spectral peaks of the human voice, and these are highly useful to distinguish vowel sounds. The characteristics of the spectral peaks are such that, these peaks are likely to survive in a signal after severe corruption by noise, and so formants are attractive features for voice activity detection under low signal-to-noise ratio (SNR) conditions. However, it is difficult to accurately extract formants from noisy signals when background noise introduces unrelated spectral peaks. Therefore, this paper proposes a simple formant-based VAD algorithm to overcome the problem of detecting formants under conditions with severe noise. The proposed method achieves a much faster processing time and outperforms standard VAD algorithms under various noise conditions. The proposed method is robust against various types of noise and produces a light computational load, so it is suitable for use in various applications. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.subject | SPECTRUM ESTIMATION | - |
dc.subject | NOISE | - |
dc.subject | ALGORITHM | - |
dc.title | Formant-Based Robust Voice Activity Detection | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yook, Dongsuk | - |
dc.identifier.doi | 10.1109/TASLP.2015.2476762 | - |
dc.identifier.scopusid | 2-s2.0-84954127731 | - |
dc.identifier.wosid | 000361752600012 | - |
dc.identifier.bibliographicCitation | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, v.23, no.12, pp.2238 - 2245 | - |
dc.relation.isPartOf | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING | - |
dc.citation.title | IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING | - |
dc.citation.volume | 23 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 2238 | - |
dc.citation.endPage | 2245 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Acoustics | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Acoustics | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | SPECTRUM ESTIMATION | - |
dc.subject.keywordPlus | NOISE | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordAuthor | Formants | - |
dc.subject.keywordAuthor | spectral peaks | - |
dc.subject.keywordAuthor | voice activity detection (VAD) | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.