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    • 34. 发明申请
    • Spoken Utterance Classification Training for a Speech Recognition System
    • 语音识别系统的语音分类训练
    • US20130159000A1
    • 2013-06-20
    • US13326659
    • 2011-12-15
    • Yun-Cheng JuJames Garnet Droppo, III
    • Yun-Cheng JuJames Garnet Droppo, III
    • G10L15/04
    • G10L15/1822
    • The subject disclosure is directed towards training a classifier for spoken utterances without relying on human-assistance. The spoken utterances may be related to a voice menu program for which a speech comprehension component interprets the spoken utterances into voice menu options. The speech comprehension component provides confirmations to some of the spoken utterances in order to accurately assign a semantic label. For each spoken utterance with a denied confirmation, the speech comprehension component automatically generates a pseudo-semantic label that is consistent with the denied confirmation and selected from a set of potential semantic labels and updates a classification model associated with the classifier using the pseudo-semantic label.
    • 主题披露旨在培训用于讲话的分类器,而不依赖人力援助。 讲话话语可能与语音菜单程序相关,语音理解组件将语音话语解释成语音菜单选项。 语音理解组件为一些语音语音提供了确认,以便准确地分配语义标签。 对于每个具有拒绝确认的口语说话,语音理解组件自动生成与拒绝确认一致的伪语义标签,并从一组潜在语义标签中选择,并使用伪语义更新与分类器相关联的分类模型 标签。
    • 37. 发明申请
    • Using Utterance Classification in Telephony and Speech Recognition Applications
    • 在电话和语音识别应用中使用语音分类
    • US20110307252A1
    • 2011-12-15
    • US12815419
    • 2010-06-15
    • Yun-Cheng JuJames Garnet Droppo, III
    • Yun-Cheng JuJames Garnet Droppo, III
    • G10L15/08
    • G10L15/1822
    • Described is the use of utterance classification based methods and other machine learning techniques to provide a telephony application or other voice menu application (e.g., an automotive application) that need not use Context-Free-Grammars to determine a user's spoken intent. A classifier receives text from an information retrieval-based speech recognizer and outputs a semantic label corresponding to the likely intent of a user's speech. The semantic label is then output, such as for use by a voice menu program in branching between menus. Also described is training, including training the language model from acoustic data without transcriptions, and training the classifier from speech-recognized acoustic data having associated semantic labels.
    • 描述了使用基于话语分类的方法和其他机器学习技术来提供不需要使用上下文自由语法来确定用户的口语意图的电话应用或其他语音菜单应用(例如,汽车应用)。 分类器从基于信息检索的语音识别器接收文本,并输出与用户言语的可能意图对应的语义标签。 然后输出语义标签,例如由菜单之间分支的语音菜单程序使用。 还描述了训练,包括从没有转录的声学数据训练语言模型,以及从具有相关联的语义标签的语音识别的声学数据训练分类器。
    • 40. 发明授权
    • Compound word splitting for directory assistance services
    • 用于目录服务的复合词分割
    • US07860707B2
    • 2010-12-28
    • US11638071
    • 2006-12-13
    • Dong YuAlejandro AceroYun-Cheng Ju
    • Dong YuAlejandro AceroYun-Cheng Ju
    • G06F17/28
    • G06F17/2755
    • A computer-implemented method is disclosed for improving the accuracy of a directory assistance system. The method includes constructing a prefix tree based on a collection of alphabetically organized words. The prefix tree is utilized as a basis for generating splitting rules for a compound word included in an index associated with the directory assistance system. A language model check and a pronunciation check are conducted in order to determine which of the generated splitting rules are mostly likely correct. The compound word is split into word components based on the most likely correct rule or rules. The word components are incorporated into a data set associated with the directory assistance system, such as into a recognition grammar and/or the index.
    • 公开了一种用于提高目录辅助系统的准确性的计算机实现的方法。 该方法包括基于字母组织的字的集合构建前缀树。 前缀树被用作为包括在与目录辅助系统相关联的索引中的复合词生成分割规则的基础。 进行语言模型检查和发音检查,以确定哪些生成的分裂规则最可能是正确的。 复合词根据最可能的正确规则或规则分为单词组成部分。 单词组件被合并到与目录辅助系统相关联的数据集中,诸如识别语法和/或索引。