Robust vocabulary recognition clustering model using an average estimator least mean square filter in noisy environments
DC Field | Value | Language |
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dc.contributor.author | Ahn, Chan-Shik | - |
dc.contributor.author | Oh, Sang-Yeob | - |
dc.date.available | 2020-02-28T16:45:51Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2014-08 | - |
dc.identifier.issn | 1617-4909 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/12435 | - |
dc.description.abstract | Noise estimation and detection algorithms must adapt to a changing environment quickly, so they use a least mean square (LMS) filter. However, there is a downside. An LMS filter is very low, and it consequently lowers speech recognition rates. In order to overcome such a weak point, we propose a method to establish a robust speech recognition clustering model for noisy environments. Since this proposed method allows the cancelation of noise with an average estimator least mean square (AELMS) filter in a noisy environment, a robust speech recognition clustering model can be established. With the AELMS filter, which can preserve source features of speech and decrease the degradation of speech information, noise in a contaminated speech signal gets canceled, and a Gaussian state model is clustered as a method to make noise more robust. By composing a Gaussian clustering model, which is a robust speech recognition clustering model, in a noisy environment, recognition performance was evaluated. The study shows that the signal-to-noise ratio of speech, which was improved by canceling environment noise that kept changing, was enhanced by 2.8 dB on average, and recognition rate improved by 4.1 %. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER LONDON LTD | - |
dc.relation.isPartOf | PERSONAL AND UBIQUITOUS COMPUTING | - |
dc.subject | END-POINT DETECTION | - |
dc.subject | SPEECH SYNTHESIS | - |
dc.subject | ALGORITHM | - |
dc.title | Robust vocabulary recognition clustering model using an average estimator least mean square filter in noisy environments | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000339891900002 | - |
dc.identifier.doi | 10.1007/s00779-013-0732-5 | - |
dc.identifier.bibliographicCitation | PERSONAL AND UBIQUITOUS COMPUTING, v.18, no.6, pp.1295 - 1301 | - |
dc.identifier.scopusid | 2-s2.0-84904799137 | - |
dc.citation.endPage | 1301 | - |
dc.citation.startPage | 1295 | - |
dc.citation.title | PERSONAL AND UBIQUITOUS COMPUTING | - |
dc.citation.volume | 18 | - |
dc.citation.number | 6 | - |
dc.contributor.affiliatedAuthor | Oh, Sang-Yeob | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | AELMS filter | - |
dc.subject.keywordAuthor | Clustering model | - |
dc.subject.keywordAuthor | Noise estimation | - |
dc.subject.keywordAuthor | Gaussian state model | - |
dc.subject.keywordPlus | END-POINT DETECTION | - |
dc.subject.keywordPlus | SPEECH SYNTHESIS | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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