基于模型混淆度的模型组合算法研究

传统的声学模型训练算法如最大似然估计(Maximum likelihood estimation, MLE), 在训练时只考虑了模型自身而没有考虑模型之间的相互影响. 为了进一步提升模型的识别效果, 区分性训练算法被提出. 本文在最小音素错误(Minimum phone error, MPE)区分性训练算法的基础上提出一种基于模型间混淆程度进行模型组合的算法: 针对单混合分量模型, 依据模型间混淆程度对MLE和MPE的模型进行加权组合; 针对多混合分量模型, 提出一种模型选择的算法来获取新的模型参数. 实验表明, 与MPE算法相比, 对单分量的情况, 该算法可以使系统的误识率相对降低4%左右; 对于多分量的情况, 该算法可以使系统的误识率相对降低3%左右.

Research on Model Combination Based on Model Confusion

Traditional training methods such as maximum likelihood estimation (MLE) do not consider discriminative relation between acoustic models, so some models are apt to obscure each other. In order to raise the differentiation degree between models, discriminative training criteria are proposed. This paper mainly introduces the new discriminative training method, the minimum phone error (MPE), and proposes a new model combination method. For the single mixture model, it weights the models of MPE and MLE based on the confusion of them. For the multimixture model, a model selection method is proposed. Experiments demonstrate that this method achieves consistent improvement over the conventional MPE training and the relative error reduction is about 4% for the single mixture model and about 3% for the multi mixture model.