Environmental Adaptation with a Small Data Set of the Target Domain

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.