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Adapting the parameters of a statistical speaker independent continuous speech recognizer to the speaker can significantly improve the recognition performance and robustness of the system. In this paper, we propose a novel target-driven speaker adaptation method, Generalized Joint Maximum a Posteriori (GJMAP), which extends and improves the previous successful method JMAP. GJMAP partitions the HMM parameters with respect to the adaptation data, using the priori phonetic knowledge. The generation of regression class trees is dynamically constructed on the target-driven principle in order to obtain the maximum increase of the auxiliary function. An off-line adaptation experiment on large vocabulary continuous speech recognition is carried out. The experimental results show GJMAP has more advantages than the conventional methods.