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    Coupled hidden Markov model (CHMM) for continuous audiovisual speech recognition
    1.
    发明授权
    Coupled hidden Markov model (CHMM) for continuous audiovisual speech recognition 有权
    耦合隐马尔可夫模型（CHMM）用于连续视听语音识别

    公开(公告)号：US07454342B2

    公开(公告)日：2008-11-18

    申请号：US10392709

    申请日：2003-03-19

    申请人： Ara Victor Nefian ,  Xiaoxing Liu ,  Xiaobo Pi ,  Luhong Liang ,  Yibao Zhao

    发明人： Ara Victor Nefian ,  Xiaoxing Liu ,  Xiaobo Pi ,  Luhong Liang ,  Yibao Zhao

    IPC分类号： G10L15/14

    CPC分类号： G06K9/6293 ,  G06K9/6297 ,  G10L15/142 ,  G10L15/25

    摘要： Method and apparatus for an audiovisual continuous speech recognition (AVCSR) system using a coupled hidden Markov model (CHMM) are described herein. In one aspect, an exemplary process includes receiving an audio data stream and a video data stream, and performing continuous speech recognition based on the audio and video data streams using a plurality of hidden Markov models (HMMs), a node of each of the HMMs at a time slot being subject to one or more nodes of related HMMs at a preceding time slot. Other methods and apparatuses are also described.

    摘要翻译： 本文描述了使用耦合隐马尔可夫模型（CHMM）的视听连续语音识别（AVCSR）系统的方法和装置。 在一个方面，示例性过程包括接收音频数据流和视频数据流，以及使用多个隐马尔可夫模型（HMM），基于音频和视频数据流执行连续语音识别，每个HMM的节点 在时隙处于前一时隙处的相关HMM的一个或多个节点。 还描述了其它方法和装置。

    Bike bag
    4.
    外观设计
    Bike bag 有权

    公开(公告)号：USD955971S1

    公开(公告)日：2022-06-28

    申请号：US29762520

    申请日：2020-12-17

    申请人： Xiaoxing Liu

    设计人： Xiaoxing Liu

    Method, apparatus, and system for building context dependent models for a large vocabulary continuous speech recognition (LVCSR) system
    6.
    发明授权
    Method, apparatus, and system for building context dependent models for a large vocabulary continuous speech recognition (LVCSR) system 有权
    用于为大型词汇连续语音识别（LVCSR）系统构建上下文相关模型的方法，装置和系统

    公开(公告)号：US07587321B2

    公开(公告)日：2009-09-08

    申请号：US10332652

    申请日：2001-05-08

    申请人： Xiaoxing Liu ,  Baosheng Yuan ,  Yonghong Yan

    发明人： Xiaoxing Liu ,  Baosheng Yuan ,  Yonghong Yan

    IPC分类号： G10L15/14

    CPC分类号： G10L15/187 ,  G10L15/1815

    摘要： According to one aspect of the invention, a method is provided in which a set of multiple mixture monophone models is created and trained to generate a set of multiple mixture context dependent models. A set of single mixture triphone models is created and trained to generate a set of context dependent models. Corresponding states of the triphone models are clustered to obtain a set of tied states based on a decision tree clustering process. Parameters of the context dependent models are estimated using a data dependent maximum a posteriori (MAP) adaptation method in which parameters of the tied states of the context dependent models are derived by adapting corresponding parameters of the context independent models using the training data associated with the respective tied states.

    摘要翻译： 根据本发明的一个方面，提供了一种方法，其中创建并训练一组多个混合单声道模型以生成一组多个混合上下文相关模型。 创建和训练了一组单一混合三音模型，以生成一组上下文相关模型。 将三通电话模型的对应状态聚类成基于决策树聚类过程获得一组绑定状态。 使用依赖于数据的最大后验（MAP）适配方法来估计上下文相关模型的参数，其中，通过使用与上下文相关模型相关联的训练数据来调整上下文无关模型的相应参数，从而导出上下文相关模型的绑定状态的参数 各自的绑定状态。

    Face mask
    7.
    外观设计
    Face mask 有权

    公开(公告)号：USD962419S1

    公开(公告)日：2022-08-30

    申请号：US29743997

    申请日：2020-07-26

    申请人： Xiaoxing Liu

    设计人： Xiaoxing Liu

    Method, apparatus, and system for building context dependent models for a large vocabulary continuous speech recognition (lvcsr) system
    8.
    发明申请
    Method, apparatus, and system for building context dependent models for a large vocabulary continuous speech recognition (lvcsr) system 有权
    用于为大型词汇连续语音识别（lvcsr）系统构建上下文相关模型的方法，装置和系统

    公开(公告)号：US20050228666A1

    公开(公告)日：2005-10-13

    申请号：US10332652

    申请日：2001-05-08

    申请人： Xiaoxing Liu ,  Baosheng Yuan ,  Yonghong Yan

    发明人： Xiaoxing Liu ,  Baosheng Yuan ,  Yonghong Yan

    IPC分类号： G10L15/18 ,  G10L15/187 ,  G10L15/14

    CPC分类号： G10L15/187 ,  G10L15/1815

    摘要： According to one aspect of the invention, a method is provided in which a set of multiple mixture monophone models is created and trained to generate a set of multiple mixture context dependent models. A set of single mixture triphone models is created and trained to generate a set of context dependent models. Corresponding states of the triphone models are clustered to obtain a set of tied states based on a decision tree clustering process. Parameters of the context dependent models are estimated using a data dependent maximum a posteriori (MAP) adaptation method in which parameters of the tied states of the context dependent models are derived by adapting corresponding parameters of the context independent models using the training data associated with the respective tied states.

    摘要翻译： 根据本发明的一个方面，提供了一种方法，其中创建并训练一组多个混合单声道模型以生成一组多个混合上下文相关模型。 创建和训练了一组单一混合三音模型，以生成一组上下文相关模型。 将三通电话模型的对应状态聚类成基于决策树聚类过程获得一组绑定状态。 使用依赖于数据的最大后验（MAP）适配方法来估计上下文相关模型的参数，其中，通过使用与上下文相关模型相关联的训练数据来调整上下文无关模型的相应参数，从而导出上下文相关模型的绑定状态的参数 各自的绑定状态。