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公开(公告)号：US4903305A
公开(公告)日：1990-02-20
申请号：US328738
申请日：1989-03-23
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。
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公开(公告)号：US5680511A
公开(公告)日：1997-10-21
申请号：US477287
申请日：1995-06-07
CPC分类号： G10L15/1815
摘要： In one aspect, the invention provides word recognition systems that operate to recognize an unrecognized or ambiguous word that occurs within a passage of words. The system can offer several words as choice words for inserting into the passage to replace the unrecognized word. The system can select the best choice word by using the choice word to extract from a reference source, sample passages of text that relate to the choice word. For example, the system can select the dictionary passage that defines the choice word. The system then compares the selected passage to the current passage, and generates a score that indicates the likelihood that the choice word would occur within that passage of text. The system can select the choice word with the best score to substitute into the passage. The passage of words being analyzed can be any word sequence including an utterance, a portion of handwritten text, a portion of typewritten text or other such sequence of words, numbers and characters. Alternative embodiments of the present invention are disclosed which function to retrieve documents from a library as a function of context.
摘要翻译： 在一个方面，本发明提供了操作以识别在单词通过内出现的未识别或不明确的单词的单词识别系统。 该系统可以提供多个单词作为选择单词，用于插入到段落中以替换未被识别的单词。 系统可以通过使用选择单词从参考源中提取出最佳选择单词，与选择单词相关的文本的样本段落。 例如，系统可以选择定义选择字的字典通道。 然后，系统将所选择的段落与当前段落进行比较，并生成一个分数，指示选择单词在文本段落内发生的可能性。 系统可以选择具有最佳分数的选择词来代替段落。 正在分析的单词的通过可以是包括发音，手写文本的一部分，打字文本的一部分或其他这样的单词，数字和字符序列的任何单词序列。 公开了本发明的替代实施例，其功能是根据上下文从库中检索文档。
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公开(公告)号：US4348553A
公开(公告)日：1982-09-07
申请号：US165466
申请日：1980-07-02
申请人： James K. Baker , Janet M. Baker
发明人： James K. Baker , Janet M. Baker
CPC分类号： G06K9/6206 , G06K9/6297 , G10L15/14
摘要： A speech recognition system is disclosed which employs a network of elementary local decision modules for matching an observed time-varying speech pattern against all possible time warpings of the stored prototype patterns. For each elementary speech segment, an elementary recognizer provides a score indicating the degree of correlation of the input speech segment with stored spectral patterns. Each local decision module receives the results of the elementary recognizer and, at the same time, receives an input from selected ones of the other local decision modules. Each local decision module specializes in a particular node in the network wherein each node matches the probability of how well the input segment of speech matches the particular sound segments in the sounds of the words spoken. Each local decision module takes the prior decisions of all preceding sound segments which are input from the other local decision modules and makes a selection of the locally optimum time warping to be permitted. By this selection technique, each speech segment is stretched or compressed by an arbitrary, nonlinear function based on the control of the interconnections of the other local decision modules to a particular local decision module. Each local decision module includes an accumulator memory which stores the logarithmic probabilities of the current observation which is conditional upon the internal event specified by a word to be matched or identifier of the particular pattern that corresponds to the subject node for that particular pattern. For each observation, these probabilities are computed and loaded into the accumulator memory of all the modules and, the result of the locally optimum time warping representing the accumulated score or network path to a node for the word with the highest probability is chosen.
摘要翻译： 公开了一种语音识别系统，其采用基本局部决策模块的网络，用于将观察到的时变语音模式与存储的原型图案的所有可能的时间变形相匹配。 对于每个基本语音段，基本识别器提供指示输入语音段与存储的频谱模式的相关程度的分数。 每个本地决策模块接收基本识别器的结果，同时从其他本地决策模块的选定接收器接收输入。 每个本地决策模块专门针对网络中的特定节点，其中每个节点匹配输入段语音与所说话语音中的特定声音段匹配的概率。 每个本地决策模块采用从其他本地决策模块输入的所有以前的声音段的先前决定，并且选择要允许的局部最佳时间翘曲。 通过这种选择技术，基于将其他局部决策模块的互连控制到特定的本地决策模块，每个语音段被任意的非线性函数拉伸或压缩。 每个本地决策模块包括累加器存储器，其存储当前观察的对数概率，该对数概率取决于要匹配的字指定的内部事件或与该特定模式的对象节点对应的特定模式的标识符。 对于每个观察，这些概率被计算并加载到所有模块的累加器存储器中，并且选择表示具有最高概率的单词的节点的累积分数或网络路径的局部最佳时间扭曲的结果。
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