声学模型区分性训练中的动态加权数据选取方法

提出了一种基于动态加权的数据选取方法, 并应用到连续语音识别的声学模型区分性训练中. 该方法联合后验概率和音素准确率选取数据, 首先, 采用后验概率的Beam算法裁剪词图, 在此基础上依据候选词所在候选路径的错误率, 基于后验概率动态的赋予候选词不同的权值; 其次, 通过统计音素对之间的混淆程度, 给易混淆音素对动态地加以不同的惩罚权重, 计算音素准确率; 最后, 在估计得到弧段期望准确率分布的基础上, 采用高斯函数形式对所有竞争弧段的期望音素准确率软加权.实验结果表明, 与最小音素错误准则相比, 该动态加权方法识别准确率提高了0.61%, 可有效减少训练时间.

A Variable Weighting Based Training Data Selection Method for Discriminative Training of Acoustic Models

By combining the phone posterior and phone accuracy, a data selection method based on variable weighting is proposed to improve the discriminative training performance of the acoustic model for continuous speech recognition. Firstly, the word lattice is reduced by using a posterior-based Beam pruning method, and for each hypothesis word a weight is derived from the word error rates of the path containing that word with the posterior. Then, each pair of confusing phones is variably weighted according to a phone confusion matrix, and the modified phone accuracy is calculated by applying those weights. Finally, the distribution of the expected phone accuracies is estimated and all competing arcs are soft weighted using Gaussian functions. Experimental results show that compared with the minimum phone error criterion, the variable weighting method not only improves the recognition rate by 0.61%, but also reduces the required training time.