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Proc. Interspeech 2010

dc.contributor.authorVipperla, Ravi Chander
dc.contributor.authorRenals, Steve
dc.contributor.authorFrankel, Joe
dc.date.accessioned2011-01-19T12:36:57Z
dc.date.available2011-01-19T12:36:57Z
dc.date.issued2010en
dc.identifier.urihttp://hdl.handle.net/1842/4661
dc.description.abstractLinear regression based speaker adaptation approaches can improve Automatic Speech Recognition (ASR) accuracy significantly for a target speaker. However, when the available adaptation data is limited to a few seconds, the accuracy of the speaker adapted models is often worse compared with speaker independent models. In this paper, we propose an approach to select a set of reference speakers acoustically close to the target speaker whose data can be used to augment the adaptation data. To determine the acoustic similarity of two speakers, we propose a distance metric based on transforming sample points in the acoustic space with the regression matrices of the two speakers. We show the validity of this approach through a speaker identification task. ASR results on SCOTUS and AMI corpora with limited adaptation data of 10 to 15 seconds augmented by data from selected reference speakers show a significant improvement in Word Error Rate over speaker independent and speaker adapted models.en
dc.titleAugmentation of adaptation dataen
dc.typeConference Paperen
rps.titleProc. Interspeech 2010en
dc.date.updated2011-01-19T12:36:57Z


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