Abstract: A discriminative framework of tone model integration into continuous speech recognition is proposed. The method uses model dependent weights to scale probabilities of the hidden Markov model based on spectral features and tone models based on tonal features. The weights are discriminatively trained by the minimum phone error criterion and update equation of model weights based on the extended Baum-Welch algorithm is derived. Variant schemes of model weight combination are evaluated and a smoothing technique is introduced to make training robust to over fitting. The proposed method is evaluated on tonal syllable output and character output speech recognition tasks. Experiments results show the proposed method has obtained 9.5% and 4.7% relative error reduction than global weight on the two tasks due to a better interpolation of the given models. This proves the effectiveness of discriminative trained model weights for tone model integration.