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Environmental Adaptation with a Small Data Set of the Target Domain

  • Andreas Maier
  • Tino Haderlein
  • Elmar Nöth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4188)

Abstract

In this work we present an approach to adapt speaker-independent recognizers to a new acoustical environment. The recognizers were trained with data which were recorded using a close-talking microphone. These recognizers are to be evaluated with distant-talking microphone data. The adaptation set was recorded with the same type of microphone. In order to keep the speaker-independency this set includes 33 speakers. The adaptation itself is done using maximum a posteriori (MAP) and maximum likelihood linear regression adaptation (MLLR) in combination with the Baum-Welch algorithm. Furthermore the close-talking training data were artificially reverberated to reduce the mismatch between training and test data. In this manner the performance could be increased from 9.9 % WA to 40.0 % WA in speaker-open conditions. If further speaker-dependent adaptation is applied this rate is increased up to 54.9 % WA.

Keywords

Target Domain Adaptation Data Environmental Adaptation Microphone Array Emotional Speech 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andreas Maier
    • 1
  • Tino Haderlein
    • 1
  • Elmar Nöth
    • 1
  1. 1.Chair for Pattern RecognitionUniversity of Erlangen NurembergErlangenGermany

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