Multi-Layer Structure MLLR Adaptation Algorithm Based on Target-Driven

In this paper, a new algorithm called Target Driven based multiple layer maximum likelihood linear regression (TMLLR) is proposed for model adaptation in speech recognition. The algorithm can be regarded as the improvement of maximum likelihood linear regression (MLLR) using the generation of regression class trees for model adaptation. Different from conventional MLLR, the regression classes of TMLLR are generated dynamically based on increment of target function and a multi layer feedback mechanism. Because of the special multi layer structure of TMLLR, some redundant computing cost can be reduced, which caused much faster adaptation speed. The target driven strategy is aimed at increasing the likelihood probability, which is same to measure of speech recognition, so a higher recognition accuracy of the system can be achieved. In comparison with the conventional MLLR using the generation of regression class tree, TMLLR achieved a further word error rate reduction by 10% and had only about half computational time consuming in supervised adaptation experiments.