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Knowledge-Rich Model Transformations for SpeakerKnowledge-Rich Model Transformations for Speaker Normalization in Speech Recognition
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
KTH, School of Computer Science and Communication (CSC), Speech, Music and Hearing, TMH, Speech Communication and Technology.
2008 (English)In: Proceedings, FONETIK 2008, Department of Linguistics, University of Gothenburg / [ed] Anders Eriksson, Jonas Lindh, 2008, 37-40 p.Conference paper, Published paper (Other academic)
Abstract [en]

In this work we extend the test utterance adaptationtechnique used in vocal tract length normalizationto a larger number of speaker characteristicfeatures. We perform partially jointestimation of four features: the VTLN warpingfactor, the corner position of the piece-wise linearwarping function, spectral tilt in voicedsegments, and model variance scaling. In experimentson the Swedish PF-Star children database,joint estimation of warping factor andvariance scaling lowers the recognition errorrate compared to warping factor alone.

Place, publisher, year, edition, pages
2008. 37-40 p.
National Category
Computer Science Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-52014ISBN: 978-91-977196-0-5 (print)OAI: oai:DiVA.org:kth-52014DiVA: diva2:465307
Conference
FONETIK 2008, Department of Linguistics, University of Gothenburg
Note
tmh_import_11_12_14Available from: 2011-12-14 Created: 2011-12-14 Last updated: 2012-01-26Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • de-DE
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  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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