关键词: 区分性模型组合, 语音识别, 多层感知器, 区分性训练

Abstract: The combination of the hidden Markov model based spectral acoustic model，multi-layer perceptron based phoneme classifier and Gaussian mixture model based tone classifier in lattice rescoring is proposed.Moreover，discriminative model weight training is applied to tune the impact of the heterogeneous models according to different phonetic contexts for better model interpolation.Experimental results on continuous speech recognition show significant improvement can be obtained using the combination of the models.Four context dependent weighting schemes for discriminative trained scaling factors are evaluated.It is also shown introducing left contexts can obtain the best recognition accuracy.Results have also shown tree based model combination is superior to the system based on feature space combination.

Key words: discriminative model combination, speech recognition, multi-layer perceptron, discriminative training