Robust parallel speech recognition in multiple energy bands
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
Using artificially reverberated training data in distant-talking ASR
TSD'05 Proceedings of the 8th international conference on Text, Speech and Dialogue
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
An Extension to the Sammon Mapping for the Robust Visualization of Speaker Dependencies
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
PEAKS - A system for the automatic evaluation of voice and speech disorders
Speech Communication
Towards the Automatic Classification of Reading Disorders in Continuous Text Passages
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
An adaptive keyboard with personalized language-based features
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
An automatic version of a reading disorder test
ACM Transactions on Speech and Language Processing (TSLP)
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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.