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    • 51. 发明授权
    • Directional optimization via EBW
    • 通过EBW定向优化
    • US08527566B2
    • 2013-09-03
    • US12777768
    • 2010-05-11
    • Dimitri KanevskyDavid NahamooBhuvana RamabhadranTara N. Sainath
    • Dimitri KanevskyDavid NahamooBhuvana RamabhadranTara N. Sainath
    • G06F7/00
    • G06F17/11
    • An optimization system and method includes determining a best gradient as a sparse direction in a function having a plurality of parameters. The sparse direction includes a direction that maximizes change of the function. This maximum change of the function is determined by performing an optimization process that gives maximum growth subject to a sparsity regularized constraint. An extended Baum Welch (EBW) method can be used to identify the sparse direction. A best step size is determined along the sparse direction by finding magnitudes of entries of direction that maximizes the function restricted to the sparse direction. A solution is recursively refined for the function optimization using a processor and storage media.
    • 优化系统和方法包括在具有多个参数的函数中确定最佳梯度作为稀疏方向。 稀疏方向包括使功能变化最大化的方向。 通过执行优化处理来确定功能的最大变化,该优化过程允许受到稀疏正则化约束的最大增长。 扩展的Baum Welch(EBW)方法可用于识别稀疏方向。 通过找到使限于稀疏方向的功能最大化的方向条目的大小,沿着稀疏方向确定最佳步长。 使用处理器和存储介质递归地优化了功能优化的解决方案。
    • 53. 发明申请
    • SPARSE REPRESENTATION FEATURES FOR SPEECH RECOGNITION
    • 用于语音识别的小数代表特征
    • US20120078621A1
    • 2012-03-29
    • US12889845
    • 2010-09-24
    • Dimitri KanevskyDavid NahamooBhuvana RamabhadranTara N. Sainath
    • Dimitri KanevskyDavid NahamooBhuvana RamabhadranTara N. Sainath
    • G10L15/00
    • G10L15/02
    • Techniques are disclosed for generating and using sparse representation features to improve speech recognition performance. In particular, principles of the invention provide sparse representation exemplar-based recognition techniques. For example, a method comprises the following steps. A test vector and a training data set associated with a speech recognition system are obtained. A subset of the training data set is selected. The test vector is mapped with the selected subset of the training data set as a linear combination that is weighted by a sparseness constraint such that a new test feature set is formed wherein the training data set is moved more closely to the test vector subject to the sparseness constraint. An acoustic model is trained on the new test feature set.The acoustic model trained on the new test feature set may be used to decode user speech input to the speech recognition system.
    • 公开了用于生成和使用稀疏表示特征以改善语音识别性能的技术。 特别地,本发明的原理提供了基于示例的稀疏表示识别技术。 例如,一种方法包括以下步骤。 获得与语音识别系统相关联的测试向量和训练数据集。 选择训练数据集的子集。 将测试向量与所选择的训练数据集的子集映射为由稀疏约束加权的线性组合,使得形成新的测试特征集合,其中训练数据集更接近地移动到受测对象的测试向量 稀疏约束 在新的测试功能集上训练声学模型。 在新测试特征集上训练的声学模型可以用于解码输入到语音识别系统的用户语音。
    • 54. 发明申请
    • MANAGING ENCOUNTERS WITH PERSONS
    • 管理人员与人
    • US20120053980A1
    • 2012-03-01
    • US12872042
    • 2010-08-31
    • Sarah H. BassonDimitri KanevskyClifford Alan PickoverTara N. Sainath
    • Sarah H. BassonDimitri KanevskyClifford Alan PickoverTara N. Sainath
    • G06Q10/00
    • G06Q10/06316
    • Techniques are disclosed for facilitating coordination of user activities in accordance with information processing systems and, more particularly, to techniques for managing encounters with persons using such information processing systems. For example, a method for facilitating user coordination of one or more activities comprises the following steps. User personal preference input for managing an encounter with at least one other person is accepted. Input of at least one user schedule entry is received. Schedule entries of the at least one other person are evaluated and it is automatically determined whether there is an overlap between the at least one user schedule entry and the schedule entries of the at least one other person. A response to a determined overlap is automatically determined. The user personal preference input may comprise an indication of whether the user wishes to avoid an encounter with the at least one other person or coordinate an encounter with the at least one other person.
    • 公开了用于促进根据信息处理系统的用户活动的协调的技术,更具体地,涉及用于管理与使用这种信息处理系统的人的遭遇的技术。 例如,用于促进一个或多个活动的用户协调的方法包括以下步骤。 用于管理与至少一个其他人的遭遇的用户个人偏好输入被接受。 接收至少一个用户日程表条目的输入。 对至少一个其他人的计划条目进行评估,并且自动确定该至少一个用户日程表条目和该至少一个其他人的日程表条目之间是否存在重叠。 自动确定对确定的重叠的响应。 用户个人偏好输入可以包括用户是否希望避免与至少一个其他人的遭遇或协调与至少一个其他人的遭遇的指示。
    • 59. 发明授权
    • Multiple audio file processing method and system
    • 多种音频文件处理方法和系统
    • US08103511B2
    • 2012-01-24
    • US12127874
    • 2008-05-28
    • Sara H. BassonBrian R. HeasmanDimitri KanevskyEdward Emile Kelley
    • Sara H. BassonBrian R. HeasmanDimitri KanevskyEdward Emile Kelley
    • G10L21/00G10L11/02
    • G10L21/02G11B2020/10546
    • An audio file generation method and system. A computing system receives a first audio file comprising first speech data associated with a first party. The computing system receives a second audio file comprising second speech data associated with a second party. The first audio file differs from the second audio file. The computing system generates a third audio file from the second audio file. The third audio file differs from the second audio file. The process to generate the third audio file includes identifying a first set of attributes missing from the second audio file and adding the first set of attributes to the second audio file. The process to generate the third audio file additionally includes removing a second set of attributes from the second audio file. The third audio file includes third speech data associated with the second party. The computing system broadcasts the third audio file.
    • 音频文件生成方法和系统。 计算系统接收包括与第一方相关联的第一语音数据的第一音频文件。 计算系统接收包括与第二方相关联的第二语音数据的第二音频文件。 第一个音频文件与第二个音频文件不同。 计算系统从第二音频文件生成第三音频文件。 第三个音频文件与第二个音频文件不同。 生成第三音频文件的过程包括识别从第二音频文件丢失的第一组属性,并将第一组属性添加到第二音频文件。 生成第三音频文件的过程另外包括从第二音频文件中移除第二组属性。 第三音频文件包括与第二方相关联的第三语音数据。 计算系统广播第三个音频文件。