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)


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.


Target Domain Adaptation Data Environmental Adaptation Microphone Array Emotional Speech 
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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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