Vocabulary gaussian clustering model using AELMS filter
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, J.-S. | - |
dc.contributor.author | Oh, S.-Y. | - |
dc.date.available | 2020-02-29T01:41:45Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/14946 | - |
dc.description.abstract | With the AELMS filter, which can preserve sources features of speech and decrease the damage on speech information, noise of a contaminated speech signal got canceled, and a gaussian model was 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 noise environment, a recognition performance was evaluated. The study shows that SNR of speech, which was gained by canceling the environment noise which was kept changing, was enhanced by 2.7dB in an average and a recognition rate was improved by 3.1%. © 2013 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.relation.isPartOf | 2013 International Conference on Information Science and Applications, ICISA 2013 | - |
dc.subject | AELMS Filter | - |
dc.subject | Clustering model | - |
dc.subject | Gaussian model | - |
dc.subject | Gaussians | - |
dc.subject | Noise environments | - |
dc.subject | Robust speech recognition | - |
dc.subject | Speech information | - |
dc.subject | Speech signals | - |
dc.subject | Cluster analysis | - |
dc.subject | Gaussian distribution | - |
dc.subject | Information science | - |
dc.subject | Speech recognition | - |
dc.subject | Signal to noise ratio | - |
dc.title | Vocabulary gaussian clustering model using AELMS filter | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.doi | 10.1109/ICISA.2013.6579392 | - |
dc.identifier.bibliographicCitation | 2013 International Conference on Information Science and Applications, ICISA 2013 | - |
dc.identifier.scopusid | 2-s2.0-84883768467 | - |
dc.citation.title | 2013 International Conference on Information Science and Applications, ICISA 2013 | - |
dc.contributor.affiliatedAuthor | Oh, S.-Y. | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | AELMS Filter | - |
dc.subject.keywordAuthor | Gaussian model | - |
dc.subject.keywordPlus | AELMS Filter | - |
dc.subject.keywordPlus | Clustering model | - |
dc.subject.keywordPlus | Gaussian model | - |
dc.subject.keywordPlus | Gaussians | - |
dc.subject.keywordPlus | Noise environments | - |
dc.subject.keywordPlus | Robust speech recognition | - |
dc.subject.keywordPlus | Speech information | - |
dc.subject.keywordPlus | Speech signals | - |
dc.subject.keywordPlus | Cluster analysis | - |
dc.subject.keywordPlus | Gaussian distribution | - |
dc.subject.keywordPlus | Information science | - |
dc.subject.keywordPlus | Speech recognition | - |
dc.subject.keywordPlus | Signal to noise ratio | - |
dc.description.journalRegisteredClass | scopus | - |
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