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SPEECH RECOGNITION SYSTEM EMPLOYING DISCRIMINATIVELY TRAINED MODELS

机译:运用差异化训练模型的语音识别系统

摘要

A speech recognition system has vocabulary word models having for each word model state both a discrete probability distribution function and a continuous probability distribution function. Word models are initially aligned with an input utterance using the discrete probability distribution functions, and an initial matching performed. From well scoring word models, a ranked scoring of those models is generated using the respective continuous probability distribution functions. After each utterance, preselected continuous probability distribution function parameters are discriminatively adjusted to increase the difference in scoring between the best scoring and the next ranking models.;In the event a user subsequently corrects a prior recognition event by selecting a different word model from that generated by the recognition system, a re-adjustment of the continuous probability distribution function parameters is performed by adjusting the current state of the parameters opposite to the adjustment performed with the original recognition event, and adjusting the current parameters to that which would have been performed if the user correction associated word had been the best scoring model.
机译:语音识别系统具有词汇单词模型,该词汇单词模型对于每个单词模型状态都具有离散的概率分布函数和连续的概率分布函数。使用离散概率分布函数,将单词模型与输入话语初始对齐,并执行初始匹配。从得分较高的单词模型中,使用相应的连续概率分布函数生成这些模型的排名得分。每次发声后,将有区别地调整预选的连续概率分布函数参数,以增加最佳评分和下一个排名模型之间的评分差异;如果用户随后通过选择与生成的单词模型不同的单词模型来纠正先前的识别事件通过识别系统,通过调整与用原始识别事件进行的调整相反的参数的当前状态,并将当前参数调整为如果可能的话,将执行连续概率分布函数参数的重新调整。用户更正相关字词是最佳评分模型。

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