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    Error correction in speech recognition by correcting text around selected area
    1.
    发明授权
    Error correction in speech recognition by correcting text around selected area 有权
    通过对所选区域的文本进行校正来进行语音识别中的纠错

    公开(公告)号：US06912498B2

    公开(公告)日：2005-06-28

    申请号：US09845769

    申请日：2001-05-02

    申请人： Daniell Stevens ,  Robert Roth ,  Joel M. Gould ,  Michael J. Newman ,  Dean Sturtevant ,  Charles E. Ingold ,  David Abrahams ,  Allan Gold

    发明人： Daniell Stevens ,  Robert Roth ,  Joel M. Gould ,  Michael J. Newman ,  Dean Sturtevant ,  Charles E. Ingold ,  David Abrahams ,  Allan Gold

    IPC分类号： G10L15/22 ,  G10L15/04

    CPC分类号： G10L15/22 ,  G10L2015/0631

    摘要： Correcting incorrect text associated with recognition errors in computer-implemented speech recognition includes receiving a selection of a word from a recognized utterance. The selection indicates a bound of a portion of the recognized utterance to be corrected. A first recognition correction is produced based on a comparison between a first alternative transcript and the recognized utterance. A second recognition correction is produced based on a comparison between a second alternative transcript and the recognized utterance. The duration of the first recognition correction differs from the duration of the second recognition correction. A portion of the recognition result that is replaced with one of the first recognition correction and the second recognition correction. includes at one bound a word indicated by the selection and extends for the duration of the one of the first recognition correction and the second recognition correction with which the portion is replaced.

    摘要翻译： 校正与计算机实现的语音识别中的识别错误相关联的错误文本包括从识别的话语中接收单词的选择。 选择表示被校正的话语的一部分的界限。 基于第一替代誊本与识别的话语之间的比较来产生第一识别校正。 基于第二替代誊本与识别的话语之间的比较来产生第二识别校正。 第一识别校正的持续时间与第二识别校正的持续时间不同。 识别结果的一部分被替换为第一识别校正和第二识别校正之一。 在一个边界处包含由选择指示的单词，并且在第一识别校正和第二识别校正中的一个的持续时间内延伸部分被替换。

    Method for representing word models for use in speech recognition
    4.
    发明授权
    Method for representing word models for use in speech recognition 失效
    用于表示用于语音识别的单词模型的方法

    公开(公告)号：US4903305A

    公开(公告)日：1990-02-20

    申请号：US328738

    申请日：1989-03-23

    申请人： Laurence Gillick ,  Dean Sturtevant ,  Robert S. Roth ,  James K. Baker ,  Janet M. Baker

    发明人： Laurence Gillick ,  Dean Sturtevant ,  Robert S. Roth ,  James K. Baker ,  Janet M. Baker

    IPC分类号： G10L15/06 ,  G10L15/14

    CPC分类号： G10L15/063 ,  G10L15/144 ,  G10L2015/0631

    摘要： A method is provided for deriving acoustic word representations for use in speech recognition. Initial word models are created, each formed of a sequence of acoustic sub-models. The acoustic sub-models from a plurality of word models are clustered, so as to group acoustically similar sub-models from different words, using, for example, the Kullback-Leibler information as a metric of similarity. Then each word is represented by cluster spelling representing the clusters into which its acoustic sub-models were placed by the clustering. Speech recognition is performed by comparing sequences of frames from speech to be recognized against sequences of acoustic models associated with the clusters of the cluster spelling of individual word models. The invention also provides a method for deriving a word representation which involves receiving a first set of frame sequences for a word, using dynamic programming to derive a corresponding initial sequence of probabilistic acoustic sub-models for the word independently of any previously derived acoustic model particular to the word, using dynamic programming to time align each of a second set of frame sequences for the word into a succession of new sub-sequences corresponding to the initial sequence of models, and using these new sub-sequences to calculate new probabilistic sub-models.

    摘要翻译： 提供了一种用于导出用于语音识别的声学词表示的方法。 创建初始词模型，每个模型由一系列声学子模型组成。 来自多个单词模型的声学子模型被聚类，以便使用例如Kullback-Leibler信息作为相似度的度量来将来自不同单词的声学上相似的子模型分组。 然后，每个单词都是用聚类拼写表示的，表示聚类中其声学子模型放置的聚类。 通过将要识别的来自语音的帧的序列与与单个词模型的群集拼写的群集相关联的声学模型的序列进行比较来执行语音识别。 本发明还提供了一种用于导出单词表示的方法，该方法涉及用于接收单词的第一组帧序列，使用动态规划来导出独立于任何先前导出的任何声学模型特定的单词的概率声学子模型的对应的初始序列 使用动态规划来将该单词的第二组帧序列中的每一个时间对齐到与模型的初始序列相对应的一系列新子序列中，并且使用这些新的子序列来计算新的概率子序列， 楷模。