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Discriminative training of HMM models using maximum margin estimation for speech recognition

机译:使用最大余量估计进行语音识别的HMM模型的判别训练

摘要

An improved discriminative training method is provided for hidden Markov models. The method includes: defining a measure of separation margin for the data; identifying a subset of training utterances having utterances misrecognized by the models; defining a training criterion for the models based on maximizing the separation margin; formulating the training criterion as a constrained minimax optimization problem; and solving the constrained minimax optimization problem over the subset of training utterances, thereby discriminatively training the models.
机译:针对隐马尔可夫模型提供了一种改进的判别训练方法。该方法包括:定义数据的分离余量的度量;以及识别训练话语的子集,这些话语具有模型无法识别的话语;基于最大化分离余量,为模型定义训练准则;将训练准则表述为约束极大极小优化问题;解决训练话语子集上的约束极大极小优化问题,从而有区别地训练模型。

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