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Conference Paper: MULTIVARIATE VOICING DECISION RULE ADAPTS TO NOISE, DISTORTION, AND SPECTRAL SHAPING.

TitleMULTIVARIATE VOICING DECISION RULE ADAPTS TO NOISE, DISTORTION, AND SPECTRAL SHAPING.
Authors
Issue Date1987
Citation
Icassp, Ieee International Conference On Acoustics, Speech And Signal Processing - Proceedings, 1987, p. 197-200 How to Cite?
AbstractAn approach to making voiced/unvoiced decisions is presented. The technique is very accurate and dynamically adapts to a wide range of environments. Reliable decisions are achieved by using a weighted sum of multiple speech parameters. Instead of using discriminant analysis to determine the optimal weights, voiced and unvoiced frames are separated into two clusters by a multivariate clustering algorithm. Since cluster analysis requires no prior voicing information, the decision rule is computed from the incoming speech rather than from a training set. An adaptive clustering algorithm is derived which continuously adjusts the weights in response to changing speech characteristics.
Persistent Identifierhttp://hdl.handle.net/10722/179566
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DC FieldValueLanguage
dc.contributor.authorThomson, David Len_US
dc.date.accessioned2012-12-19T09:59:53Z-
dc.date.available2012-12-19T09:59:53Z-
dc.date.issued1987en_US
dc.identifier.citationIcassp, Ieee International Conference On Acoustics, Speech And Signal Processing - Proceedings, 1987, p. 197-200en_US
dc.identifier.issn0736-7791en_US
dc.identifier.urihttp://hdl.handle.net/10722/179566-
dc.description.abstractAn approach to making voiced/unvoiced decisions is presented. The technique is very accurate and dynamically adapts to a wide range of environments. Reliable decisions are achieved by using a weighted sum of multiple speech parameters. Instead of using discriminant analysis to determine the optimal weights, voiced and unvoiced frames are separated into two clusters by a multivariate clustering algorithm. Since cluster analysis requires no prior voicing information, the decision rule is computed from the incoming speech rather than from a training set. An adaptive clustering algorithm is derived which continuously adjusts the weights in response to changing speech characteristics.en_US
dc.languageengen_US
dc.relation.ispartofICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedingsen_US
dc.titleMULTIVARIATE VOICING DECISION RULE ADAPTS TO NOISE, DISTORTION, AND SPECTRAL SHAPING.en_US
dc.typeConference_Paperen_US
dc.identifier.emailThomson, David L: dthomson@hku.hken_US
dc.identifier.authorityThomson, David L=rp00788en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-0023166886en_US
dc.identifier.spage197en_US
dc.identifier.epage200en_US
dc.identifier.scopusauthoridThomson, David L=7202586830en_US

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