Robust Voice Mining Techniques for Telephone Conversations

dc.contributor.advisorEspy-Wilson, Carol Y.en_US
dc.contributor.authorManocha, Sandeepen_US
dc.contributor.departmentElectrical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2006-09-12T05:51:05Z
dc.date.available2006-09-12T05:51:05Z
dc.date.issued2006-07-28en_US
dc.description.abstractVoice mining involves speaker detection in a set of multi-speaker files. In published work, training data is used for constructing target speaker models. In this study, a new voice mining scenario was considered, where there is no demarcation between training and testing data and prior target speaker models are absent. Given a database of telephone conversations, the task is to identify conversations having one or more speakers in common. Various approaches including semi-automatic and fully automatic techniques were explored and different scoring strategies were considered. Given the poor audio quality, automatic speaker segmentation is not very effective. A new technique was developed which does not require speaker segmentation by training a multi-speaker model on the entire conversation. This technique is more robust and it outperforms the automatic speaker segmentation approach. On the ENRON database, the EER is 15.98% and 6.25% for at least one and two speakers in common, respectively.en_US
dc.format.extent1302693 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/3827
dc.language.isoen_US
dc.subject.pqcontrolledEngineering, Electronics and Electricalen_US
dc.subject.pquncontrolledvoice miningen_US
dc.subject.pquncontrolledspeaker detectionen_US
dc.subject.pquncontrolledspeaker recognitionen_US
dc.titleRobust Voice Mining Techniques for Telephone Conversationsen_US
dc.typeThesisen_US

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