Tone model (TM) integration is an important task for mandarin speech recognition.It has been proved to be effective to use discriminatively trained scaling factors when integrating TM scores into multi-pass speech recognition.Moreover,context-dependent (CD) scaling can be applied for better interpolation between the models.One limitation of this approach is a large number of parameters will be introduced,which makes the technique prone to overtraining.In this paper,we propose to induce context-dependent model weights by using automatically derived phonetic decision trees.Question at each tree node is chosen tominimize the expected recognition error on the training data.First order approximation of theminimum phone error (MPE) objective function is used for question pruning to make tree building efficient.Experimental results on continuous mandarin speech recognition show the method is capable of inducing the most crucial phonetic contexts and obtains significant error reduction with far fewer parameters,compared with that obtained by using manually designed context-dependent scaling parameters. Tone model (TM) integration is an important task for mandarin speech recognition. It has been proved to have effective to use effectively discriminatively trained scaling factors when integrating TM scores into multi-pass speech recognition. Moreover, context-dependent applied for better interpolation between the models. One limitation of this approach is a large number of parameters will be introduced, which makes the technique prone to overtraining.In this paper, we propose to induce context-dependent model weights by using automatically derived phonetic decision trees.Question at each tree node is chosen tominimize the expected recognition error on the training data. First order approximation of the minimum phone error (MPE) objective function is used for question pruning to make tree building efficient. Experimental results on continuous mandarin speech recognition show the method is capable of inducing the most crucial phonetic contexts and obtaining significant error reduction with fa r fewer parameters, compared with that obtained by using manually designed context-dependent scaling parameters.