Speaker adaptation is critical for modern speech recognition systems. Due to the computational and multi-channel model sharing considerations, the use of model adaptation techniques is limited in telephony speech recognition systems. On the other hand, feature space adaptation methods such as feature space maximum likelihood linear regression (fMLLR) are effi\u0002cient approaches suitable for telephony systems. In this work, we fi\u0002rst describe techniques for ef\u0002ficient implementation of online fMLLR adaptation. Then feature space maximum a posteriori linear regression (fMAPLR) is proposed to incorporate prior knowledge for the feature transform estimation and improve the robustness of the conventional fMLLR approach. Experiments on telephony data indicate that fMAPLR is significantly more robust than fMLLR, and outperforms fMLLR especially when the adaptation data is very limited.<\/p>\n","protected":false},"excerpt":{"rendered":"
Speaker adaptation is critical for modern speech recognition systems. Due to the computational and multi-channel model sharing considerations, the use of model adaptation techniques is limited in telephony speech recognition systems. On the other hand, feature space adaptation methods such as feature space maximum likelihood linear regression (fMLLR) are effi\u0002cient approaches suitable for telephony systems. 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