COMPUTATIONAL MODELS FOR SUPPORTING SITUATED INTERACTIONS IN MULTI-USER SCENARIOS
    1.
    发明申请
    COMPUTATIONAL MODELS FOR SUPPORTING SITUATED INTERACTIONS IN MULTI-USER SCENARIOS 有权
    用于支持多用户场景中的状态交互的计算模型

    公开(公告)号:US20100332648A1

    公开(公告)日:2010-12-30

    申请号:US12492812

    申请日:2009-06-26

    IPC分类号: G06F15/173

    CPC分类号: G06Q10/10

    摘要: Individuals may interact with automated services as one or more parties, where such individuals may have collective (as well as individual) intents. Moreover, parties may concurrently communicate with the interface, and the interface may have to manage several concurrent interactions with different parties. Single-individual interfaces may be unable to react robustly to such dynamic and complex real-world scenarios. Instead, multi-party interfaces to service components may be devised that identify individuals within a scene, associate the individuals with parties, track a set of interactions of the parties with the service component, and direct the service component in interacting with the parties. A multi-party interface may also detect and politely handle interruptions, and may identify information items about individuals and parties based on context and history, prioritize the intents of the individuals and parties, and triage interactions accordingly.

    摘要翻译: 个人可以作为一个或多个缔约方与自动化服务进行交互,其中这些个人可以具有集体(以及个人)意图。 此外,各方可以同时与界面通信,并且接口可能必须管理与不同方的多个并发交互。 单个个体接口可能无法对这种动态和复杂的现实世界情景进行强大的反应。 相反,可以设计出针对服务组件的多方接口,其识别场景内的个人,将个人与各方相关联,跟踪各方与服务组件的一组交互,以及指示服务组件与各方进行交互。 多方接口还可以检测和礼貌地处理中断,并且可以基于上下文和历史识别关于个人和方的信息项,优先考虑个人和各方的意图,并相应地分类交互。

    STRUCTURED MODELS OF REPITITION FOR SPEECH RECOGNITION
    2.
    发明申请
    STRUCTURED MODELS OF REPITITION FOR SPEECH RECOGNITION 有权
    用于语音识别的结构化复制模型

    公开(公告)号:US20100076765A1

    公开(公告)日:2010-03-25

    申请号:US12233826

    申请日:2008-09-19

    IPC分类号: G10L15/00

    CPC分类号: G10L15/1822

    摘要: Described is a technology by which a structured model of repetition is used to determine the words spoken by a user, and/or a corresponding database entry, based in part on a prior utterance. For a repeated utterance, a joint probability analysis is performed on (at least some of) the corresponding word sequences as recognized by one or more recognizers) and associated acoustic data. For example, a generative probabilistic model, or a maximum entropy model may be used in the analysis. The second utterance may be a repetition of the first utterance using the exact words, or another structural transformation thereof relative to the first utterance, such as an extension that adds one or more words, a truncation that removes one or more words, or a whole or partial spelling of one or more words.

    摘要翻译: 描述了一种技术,通过该技术,部分地基于先前的话语,使用结构化重复模型来确定用户说出的单词和/或相应的数据库条目。 对于重复的话语,对由一个或多个识别器识别的相应字序列(和至少一些)和相关联的声学数据进行联合概率分析。 例如,可以在分析中使用生成概率模型或最大熵模型。 第二个发音可以是使用精确的单词或相对于第一个发音的其他结构变换的第一个发音的重复,例如添加一个或多个单词的扩展,删除一个或多个单词的截断或整个 或一个或多个单词的部分拼写。

    Structured models of repetition for speech recognition
    3.
    发明授权
    Structured models of repetition for speech recognition 有权
    用于语音识别的重复结构化模型

    公开(公告)号:US08965765B2

    公开(公告)日:2015-02-24

    申请号:US12233826

    申请日:2008-09-19

    IPC分类号: G10L15/00 G10L15/18

    CPC分类号: G10L15/1822

    摘要: Described is a technology by which a structured model of repetition is used to determine the words spoken by a user, and/or a corresponding database entry, based in part on a prior utterance. For a repeated utterance, a joint probability analysis is performed on (at least some of) the corresponding word sequences as recognized by one or more recognizers) and associated acoustic data. For example, a generative probabilistic model, or a maximum entropy model may be used in the analysis. The second utterance may be a repetition of the first utterance using the exact words, or another structural transformation thereof relative to the first utterance, such as an extension that adds one or more words, a truncation that removes one or more words, or a whole or partial spelling of one or more words.

    摘要翻译: 描述了一种技术,通过该技术,部分地基于先前的话语,使用结构化重复模型来确定用户说出的单词和/或相应的数据库条目。 对于重复的话语,对由一个或多个识别器识别的相应字序列(和至少一些)和相关联的声学数据进行联合概率分析。 例如,可以在分析中使用生成概率模型或最大熵模型。 第二个发音可以是使用精确的单词或相对于第一个发音的其他结构变换的第一个发音的重复,例如添加一个或多个单词的扩展,删除一个或多个单词的截断或整个 或一个或多个单词的部分拼写。

    Computational models for supporting situated interactions in multi-user scenarios
    4.
    发明授权
    Computational models for supporting situated interactions in multi-user scenarios 有权
    用于支持多用户场景中的定位交互的计算模型

    公开(公告)号:US08473420B2

    公开(公告)日:2013-06-25

    申请号:US12492812

    申请日:2009-06-26

    IPC分类号: G06Q10/00

    CPC分类号: G06Q10/10

    摘要: Individuals may interact with automated services as one or more parties, where such individuals may have collective (as well as individual) intents. Moreover, parties may concurrently communicate with the interface, and the interface may have to manage several concurrent interactions with different parties. Single-individual interfaces may be unable to react robustly to such dynamic and complex real-world scenarios. Instead, multi-party interfaces to service components may be devised that identify individuals within a scene, associate the individuals with parties, track a set of interactions of the parties with the service component, and direct the service component in interacting with the parties. A multi-party interface may also detect and politely handle interruptions, and may identify information items about individuals and parties based on context and history, prioritize the intents of the individuals and parties, and triage interactions accordingly.

    摘要翻译: 个人可以作为一个或多个缔约方与自动化服务进行交互,其中这些个人可以具有集体(以及个人)意图。 此外,各方可以同时与界面通信,并且接口可能必须管理与不同方的多个并发交互。 单个个体接口可能无法对这种动态和复杂的现实世界情景进行强大的反应。 相反,可以设计出针对服务组件的多方接口,其识别场景内的个体,将个人与各方相关联,跟踪各方与服务组件的一组交互,以及指导服务组件与各方进行交互。 多方接口还可以检测和礼貌地处理中断,并且可以基于上下文和历史识别关于个人和方的信息项,优先考虑个人和各方的意图,并相应地分类交互。