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    • 5. 发明申请
    • SPEECH RECOGNITION USING MULTIPLE LANGUAGE MODELS
    • 使用多种语言模型进行语音识别
    • US20120271631A1
    • 2012-10-25
    • US13450861
    • 2012-04-19
    • Fuliang WengZhe FengKui XuLin Zhao
    • Fuliang WengZhe FengKui XuLin Zhao
    • G10L15/06
    • G10L15/32G10L15/063G10L15/18G10L15/193G10L15/197G10L15/30
    • In accordance with one embodiment, a method of generating language models for speech recognition includes identifying a plurality of utterances in training data corresponding to speech, generating a frequency count of each utterance in the plurality of utterances, generating a high-frequency plurality of utterances from the plurality of utterances having a frequency that exceeds a predetermined frequency threshold, generating a low-frequency plurality of utterances from the plurality of utterances having a frequency that is below the predetermined frequency threshold, generating a grammar-based language model using the high-frequency plurality of utterances as training data, and generating a statistical language model using the low-frequency plurality of utterances as training data.
    • 根据一个实施例,一种生成用于语音识别的语言模型的方法包括:识别与语音相对应的训练数据中的多个话语,产生多个话语中的每个发声的频率计数,从多个话语中产生高频多个话语 所述多个话音具有超过预定频率阈值的频率,从具有低于所述预定频率阈值的频率的所述多个话语中产生低频多个话语,使用所述高频率生成基于语法的语言模型 多个话语作为训练数据,并且使用低频多个话语生成统计语言模型作为训练数据。
    • 6. 发明授权
    • Dialogue management using scripts and combined confidence scores
    • 对话管理使用脚本和组合的置信分数
    • US07904297B2
    • 2011-03-08
    • US11298765
    • 2005-12-08
    • Danilo MirkovicLawrence CavedonMatthew PurverFlorin RatiuTobias ScheideckFuliang WengQi ZhangKui Xu
    • Danilo MirkovicLawrence CavedonMatthew PurverFlorin RatiuTobias ScheideckFuliang WengQi ZhangKui Xu
    • G10L15/00G10L15/08
    • G06F17/28G10L2015/228
    • Representation-neutral dialogue systems and methods (“RNDS”) are described that include multi-application, multi-device spoken-language dialogue systems based on the information-state update approach. The RNDS includes representation-neutral core components of a dialogue system that provide scripted domain-specific extensions to routines such as dialogue move modeling and reference resolution, easy substitution of specific semantic representations and associated routines, and clean interfaces to external components for language-understanding (i.e., speech-recognition and parsing) and language-generation, and to domain-specific knowledge sources. The RNDS also resolves multi-device dialogue by evaluating and selecting among candidate dialogue moves based on features at multiple levels. Multiple sources of information are combined, multiple speech recognition and parsing hypotheses tested, and multiple device and moves considered to choose the highest scoring hypothesis overall. Confirmation and clarification behavior can be governed by the overall score.
    • 描述了中立的对话系统和方法(“RNDS”),其包括基于信息状态更新方法的多应用,多设备语言对话系统。 RNDS包括对话系统的代表性中立的核心组件,其提供脚本特定的例程扩展,例如对话移动建模和参考解析,容易地替换特定的语义表示和相关联的例程,以及将外部组件的界面清理为语言理解 (即语音识别和解析)和语言生成以及针对领域的知识来源。 RNDS还通过基于多层次的特征评估和选择候选对话移动来解决多设备对话。 多个信息来源相结合,多个语音识别和解析假设被测试,多个设备和移动被认为是选择最高的得分假设。 确认和澄清行为可以由总体评分来决定。
    • 10. 发明申请
    • Dialogue management using scripts and combined confidence scores
    • 对话管理使用脚本和组合的置信分数
    • US20060271364A1
    • 2006-11-30
    • US11298765
    • 2005-12-08
    • Danilo MirkovicLawrence CavedonMatthew PurverFlorin RatiuTobias ScheideckFuliang WengQi ZhangKui Xu
    • Danilo MirkovicLawrence CavedonMatthew PurverFlorin RatiuTobias ScheideckFuliang WengQi ZhangKui Xu
    • G10L15/00
    • G06F17/28G10L2015/228
    • Representation-neutral dialogue systems and methods (“RNDS”) are described that include multi-application, multi-device spoken-language dialogue systems based on the information-state update approach. The RNDS includes representation-neutral core components of a dialogue system that provide scripted domain-specific extensions to routines such as dialogue move modeling and reference resolution, easy substitution of specific semantic representations and associated routines, and clean interfaces to external components for language-understanding (i.e., speech-recognition and parsing) and language-generation, and to domain-specific knowledge sources. The RNDS also resolves multi-device dialogue by evaluating and selecting among candidate dialogue moves based on features at multiple levels. Multiple sources of information are combined, multiple speech recognition and parsing hypotheses tested, and multiple device and moves considered to choose the highest scoring hypothesis overall. Confirmation and clarification behaviour can be governed by the overall score.
    • 描述了中立的对话系统和方法(“RNDS”),其包括基于信息状态更新方法的多应用,多设备语言对话系统。 RNDS包括对话系统的代表性中立的核心组件,其提供脚本特定的例程扩展,例如对话移动建模和参考解析,容易地替换特定的语义表示和相关联的例程,以及将外部组件的界面清理为语言理解 (即语音识别和解析)和语言生成以及针对领域的知识来源。 RNDS还通过基于多层次的特征评估和选择候选对话移动来解决多设备对话。 多个信息来源相结合,多个语音识别和解析假设被测试,多个设备和移动被认为是选择最高的得分假设。 确认和澄清行为可以由总体评分来决定。