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Word recognition system which stores two models for some words and allows selective deletion of one such model

机译:单词识别系统,它为某些单词存储两个模型,并允许有选择地删除一个这样的模型

摘要

A computerized word recognition system, such as a speech recognition system, stores word models of a first and second set for each of a plurality of vocabulary words. The system has a user interface which enable a user to selectively prevent the use of a second set's word model for a selected word. Often the first set of word models are spelled word models, such as models represented by a sequence of phonetic component models, each of is derived from similar speech sounds occurring in different words. In such systems the second set of word models are custom words models derived largely from word signals which are presumed to correspond only to the model's associated word. In many embodiments, the user interface allows a user to select to stop using a selected word's custom, or second set, model by selecting a menu or control window of a user interface. It is preferred that the system automatically create custom models when word signals presumed to correspond to a given word score poorly against the word's spelled model. It is also preferred that the system respond to a command to delete a word's custom model by increasing, in subsequent adaptive training of word components for the word's spelled model, the weight given to information from such subsequent word signals relative to model information previously associated with such word components. This is done, in such a case, to compensate for the fact that past training data for the word has probably been corrupted.
机译:计算机化的单词识别系统,例如语音识别系统,为多个词汇中的每个单词存储第一组和第二组的单词模型。该系统具有用户界面,该用户界面使用户能够选择性地防止将第二组的单词模型用于所选单词。通常,第一组单词模型是拼写单词模型,例如由一系列语音成分模型表示的模型,每个单词模型都是从出现在不同单词中的相似语音中得出的。在这样的系统中,第二组单词模型是主要从单词信号中推导出的定制单词模型,这些单词信号被认为仅对应于模型的关联单词。在许多实施例中,用户界面允许用户通过选择用户界面的菜单或控制窗口来选择停止使用所选单词的习惯或第二集合模型。优选的是,当假定单词信号相对于单词的拼写模型差于给定单词分数时,系统自动创建定制模型。还优选的是,该系统通过在针对单词的拼写模型的单词成分的后续自适应训练中,相对于先前与之相关联的模型信息增加来自此类后续单词信号的信息的权重,来响应删除单词的定制模型的命令。这样的词组成。在这种情况下,这样做是为了补偿该单词的过去训练数据可能已损坏的事实。

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