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Method of speech recognition using decoded state sequences having constrained state likelihoods

机译:使用具有受限状态似然的解码状态序列的语音识别方法

摘要

Embodiments of the invention include a speech recognition method and system for transmitting information including the receipt and decoding of speech information such as that modeled by hidden Markov models (HMMs). The state likelihoods of the modeled state sequences contained within the speech information are assigned penalties based on the difference between those state likelihoods and a maximum possible state likelihood. Once penalties have been assigned, the modified state sequence with the modified state likelihoods having the highest cumulative state likelihoods is used in further speech recognition processing. In this manner, state sequences having no extremely poor state likelihoods are favored over those having both extremely high and extremely poor state likelihoods. IMAGE
机译:本发明的实施例包括用于传输信息的语音识别方法和系统,该信息包括语音信息的接收和解码,诸如通过隐马尔可夫模型(HMM)建模的信息。包含在语音信息中的建模状态序列的状态似然度基于这些状态似然度与最大可能状态似然度之间的差而被分配惩罚。一旦分配了罚分,则在进一步的语音识别处理中使用具有最高累积状态似然性的修改状态似然性的修改状态序列。以这种方式,具有极高的状态可能性的状态序列比具有极高的状态可能性和极差的状态序列的状态序列更受青睐。 <图像>

著录项

  • 公开/公告号EP0786761A3

    专利类型

  • 公开/公告日1998-09-09

    原文格式PDF

  • 申请/专利权人 AT&T CORP.;

    申请/专利号EP19970300185

  • 发明设计人 ZELJKOVICILIJA;

    申请日1997-01-14

  • 分类号G10L5/06;

  • 国家 EP

  • 入库时间 2022-08-22 02:50:04

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