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11. 发明授权

US07856351B2 Integrated speech recognition and semantic classification 有权
标题翻译：综合语音识别和语义分类
公开(公告)号：US07856351B2
公开(公告)日：2010-12-21
申请号：US11655703
申请日：2007-01-19
申请人： Sibel Yaman , Li Deng , Dong Yu , Ye-Yi Wang , Alejandro Acero
发明人： Sibel Yaman , Li Deng , Dong Yu , Ye-Yi Wang , Alejandro Acero
IPC分类号： G06F17/27
CPC分类号： G10L15/1815
摘要： A novel system integrates speech recognition and semantic classification, so that acoustic scores in a speech recognizer that accepts spoken utterances may be taken into account when training both language models and semantic classification models. For example, a joint association score may be defined that is indicative of a correspondence of a semantic class and a word sequence for an acoustic signal. The joint association score may incorporate parameters such as weighting parameters for signal-to-class modeling of the acoustic signal, language model parameters and scores, and acoustic model parameters and scores. The parameters may be revised to raise the joint association score of a target word sequence with a target semantic class relative to the joint association score of a competitor word sequence with the target semantic class. The parameters may be designed so that the semantic classification errors in the training data are minimized.
摘要翻译：一种新颖的系统集成了语音识别和语义分类，从而在训练语言模型和语义分类模型时，可以考虑接受讲话语音的语音识别器中的声学分数。例如，可以定义联合关联分数，其表示声学信号的语义类别和单词序列的对应关系。联合关联分数可以包括参数，例如声信号的信号到类建模的加权参数，语言模型参数和分数，以及声学模型参数和分数。可以修改参数以相对于具有目标语义类的竞争者词序列的联合关联分数来提高具有目标语义类别的目标词序列的联合关联分数。可以设计参数，使得训练数据中的语义分类误差最小化。

12. 发明申请

US20100256977A1 MAXIMUM ENTROPY MODEL WITH CONTINUOUS FEATURES 审中-公开
标题翻译：具有连续特征的最大熵模型
公开(公告)号：US20100256977A1
公开(公告)日：2010-10-07
申请号：US12416161
申请日：2009-04-01
申请人： Dong Yu , Li Deng , Alejandro Acero
发明人： Dong Yu , Li Deng , Alejandro Acero
IPC分类号： G10L15/00
CPC分类号： G10L15/14 , G06K9/6277 , G06K9/6297
摘要： Described is a technology by which a maximum entropy (MaxEnt) model, such as used as a classifier or in a conditional random field or hidden conditional random field that embed the maximum entropy model, uses continuous features with continuous weights that are continuous functions of the feature values (instead of single-valued weights). The continuous weights may be approximated by a spline-based solution. In general, this converts the optimization problem into a standard log-linear optimization problem without continuous weights at a higher-dimensional space.
摘要翻译：描述了最大熵（MaxEnt）模型，例如用作分类器或嵌入最大熵模型的条件随机场或隐藏条件随机场的最大熵（MaxEnt）模型使用具有连续权重的连续特征，连续权重是连续权重，特征值（而不是单值权重）。连续权重可以通过基于样条的解决方案近似。一般来说，这将优化问题转化为标准的对数线性优化问题，而在较高维度的空间则没有连续权重。

13. 发明授权

US07769582B2 Method of pattern recognition using noise reduction uncertainty 有权
标题翻译：使用降噪不确定度的模式识别方法
公开(公告)号：US07769582B2
公开(公告)日：2010-08-03
申请号：US12180260
申请日：2008-07-25
申请人： James G. Droppo , Alejandro Acero , Li Deng
发明人： James G. Droppo , Alejandro Acero , Li Deng
IPC分类号： G10L15/20 , G10L21/02 , G10L15/14
CPC分类号： G10L21/0208 , G10L15/20
摘要： A method and apparatus are provided for using the uncertainty of a noise-removal process during pattern recognition. In particular, noise is removed from a representation of a portion of a noisy signal to produce a representation of a cleaned signal. In the meantime, an uncertainty associated with the noise removal is computed and is used with the representation of the cleaned signal to modify a probability for a phonetic state in the recognition system. In particular embodiments, the uncertainty is used to modify a probability distribution, by increasing the variance in each Gaussian distribution by the amount equal to the estimated variance of the cleaned signal, which is used in decoding the phonetic state sequence in a pattern recognition task.
摘要翻译：提供了一种在模式识别期间使用噪声去除处理的不确定性的方法和装置。特别地，从噪声信号的一部分的表示中去除噪声以产生清洁信号的表示。同时，计算与噪声去除有关的不确定性，并与清除信号的表示一起使用以修改识别系统中语音状态的概率。在特定实施例中，不确定性用于通过将每个高斯分布中的方差增加等于在模式识别任务中对语音状态序列进行解码所使用的清除信号的估计方差的量来修改概率分布。

14. 发明申请

US20100076758A1 PHASE SENSITIVE MODEL ADAPTATION FOR NOISY SPEECH RECOGNITION 有权
标题翻译：语音识别的相敏感模型适应
公开(公告)号：US20100076758A1
公开(公告)日：2010-03-25
申请号：US12236530
申请日：2008-09-24
申请人： Jinyu Li , Li Deng , Dong Yu , Yifan Gong , Alejandro Acero
发明人： Jinyu Li , Li Deng , Dong Yu , Yifan Gong , Alejandro Acero
IPC分类号： G10L15/20 , G10L15/14
CPC分类号： G10L15/065 , G10L15/20
摘要： A speech recognition system described herein includes a receiver component that receives a distorted speech utterance. The speech recognition also includes an updater component that is in communication with a first model and a second model, wherein the updater component automatically updates parameters of the second model based at least in part upon joint estimates of additive and convolutive distortions output by the first model, wherein the joint estimates of additive and convolutive distortions are estimates of distortions based on a phase-sensitive model in the speech utterance received by the receiver component. Further, distortions other than additive and convolutive distortions, including other stationary and nonstationary sources, can also be estimated used to update the parameters of the second model.
摘要翻译：本文描述的语音识别系统包括接收失真的语音话语的接收机组件。所述语音识别还包括与第一模型和第二模型通信的更新器组件，其中所述更新器组件至少部分地基于由所述第一模型输出的加法和卷积失真的联合估计来自动更新所述第二模型的参数其中，加法和卷积失真的联合估计是基于由接收器部件接收的语音发声中的相敏模型的失真估计。此外，还可以估计用于更新第二模型参数的除加法和卷积失真之外的失真，包括其他静止和非平稳源。

15. 发明申请

US20100070280A1 PARAMETER CLUSTERING AND SHARING FOR VARIABLE-PARAMETER HIDDEN MARKOV MODELS 有权
标题翻译：参数聚类和共享可变参数隐藏式MARKOV模型
公开(公告)号：US20100070280A1
公开(公告)日：2010-03-18
申请号：US12211115
申请日：2008-09-16
申请人： Dong Yu , Li Deng , Yifan Gong , Alejandro Acero
发明人： Dong Yu , Li Deng , Yifan Gong , Alejandro Acero
IPC分类号： G10L15/14
CPC分类号： G10L15/142
摘要： A speech recognition system uses Gaussian mixture variable-parameter hidden Markov models (VPHMMs) to recognize speech. The VPHMMs include Gaussian parameters that vary as a function of at least one environmental conditioning parameter. The relationship of each Gaussian parameter to the environmental conditioning parameter(s) is modeled using a piecewise fitting approach, such as by using spline functions. In a training phase, the recognition system can use clustering to identify classes of spline functions, each class grouping together spline functions which are similar to each other based on some distance measure. The recognition system can then store sets of spline parameters that represent respective classes of spline functions. An instance of a spline function that belongs to a class can make reference to an associated shared set of spline parameters. The Gaussian parameters can be represented in an efficient form that accommodates the use of sharing in the above-summarized manner.
摘要翻译：语音识别系统使用高斯混合可变参数隐马尔可夫模型（VPHMM）来识别语音。 VPHMM包括作为至少一个环境调节参数的函数而变化的高斯参数。每个高斯参数与环境条件参数的关系使用分段拟合方法建模，例如通过使用样条函数。在训练阶段，识别系统可以使用聚类来识别样条函数的类别，每个类别根据一些距离度量将彼此相似的样条函数分组在一起。识别系统然后可以存储表示各种样条函数的样条参数集合。属于类的样条函数的一个实例可以引用相关联的一组样条参数。高斯参数可以以适合以上述方式共享使用的有效形式来表示。

16. 发明授权

US07680663B2 Using a discretized, higher order representation of hidden dynamic variables for speech recognition 有权
标题翻译：使用离散化的高阶表示隐藏的动态变量进行语音识别
公开(公告)号：US07680663B2
公开(公告)日：2010-03-16
申请号：US11507169
申请日：2006-08-21
申请人： Li Deng
发明人： Li Deng
IPC分类号： G10L15/28 , G10L15/00
CPC分类号： G10L15/02 , G10L2015/025
摘要： A hidden dynamics value in speech is represented by a higher order, discretized dynamic model, which predicts the discretized dynamic variable that changes over time. Parameters are trained for the model. A decoder algorithm is developed for estimating the underlying phonological speech units in sequence that correspond to the observed speech signal using the higher order, discretized dynamic model.
摘要翻译：言语中的隐藏的动态值由较高阶离散的动态模型表示，该动态模型预测随时间变化的离散化动态变量。为模型训练参数。开发了一种解码器算法，用于使用较高阶的离散动态模型来估计与观察到的语音信号相对应的潜在语音语音单元。

17. 发明授权

US07531662B2 Cinchona-alkaloid-based catalysts, and asymmetric alcoholysis of cyclic anhydrides using them 有权
标题翻译： Cinchona-生物碱类催化剂和使用它们的环状酸酐的不对称醇解
公开(公告)号：US07531662B2
公开(公告)日：2009-05-12
申请号：US10865490
申请日：2004-06-10
申请人： Li Deng , Xiaofeng Liu
发明人： Li Deng , Xiaofeng Liu
IPC分类号： C07D453/04 , A61K31/47
CPC分类号： C07D453/04
摘要： One aspect of the present invention relates to cinchona-alkaloid-based catalysts. A second aspect of the invention relates to a method of preparing a derivatized cinchona alkaloid catalyst by reacting a cinchona-alkaloid with base and a compound that has a suitable leaving group. Another aspect of the present invention relates to a method of preparing a chiral, non-racemic compound from a prochiral cyclic anhydride or a meso cyclic anhydride, comprising the step of: reacting a prochiral cyclic anhydride or a meso cyclic anhydride with a nucleophile in the presence of a catalyst; wherein said prochiral cyclic anhydride or meso cyclic anhydride comprises an internal plane of symmetry or point of symmetry or both; thereby producing a chiral, non-racemic compound; wherein said catalyst is a derivatized cinchona-alkaloid. Yet another aspect of the present invention relates to a method of kinetic resolution, comprising the step of: reacting a racemic cyclic anhydride with an alcohol in the presence of a derivatized cinchona-alkaloid catalyst.
摘要翻译：本发明的一个方面涉及基于激素 - 生物碱的催化剂。本发明的第二方面涉及一种通过使菲律宾生物碱与碱和具有合适的离去基团的化合物反应来制备衍生化的金鸡纳生物碱催化剂的方法。本发明的另一方面涉及从前手性环状酸酐或内消旋环状酸酐制备手性非外消旋化合物的方法，该方法包括以下步骤：使前手性环状酸酐或内消旋环酸酐与亲核试剂在存在催化剂; 其中所述前手性环酐或内消旋环酐包括对称性或对称点的内部平面或两者; 从而产生手性，非外消旋化合物; 其中所述催化剂是衍生化的致敏细菌 - 生物碱。本发明的另一方面涉及一种动力学拆分的方法，其包括以下步骤：在衍生化的致敏细菌 - 生物碱催化剂存在下使外消旋环酐与醇反应。

18. 发明申请

US20090070112A1 Automatic reading tutoring 有权
标题翻译：自动阅读辅导
公开(公告)号：US20090070112A1
公开(公告)日：2009-03-12
申请号：US11900274
申请日：2007-09-11
申请人： Xiaolong Li , Li Deng , Yun-Cheng Ju , Alex Acero
发明人： Xiaolong Li , Li Deng , Yun-Cheng Ju , Alex Acero
IPC分类号： G10L15/18
CPC分类号： G10L15/18 , G09B17/003 , G10L15/183
摘要： A method of providing automatic reading tutoring is disclosed. The method includes retrieving a textual indication of a story from a data store and creating a language model including constructing a target context free grammar indicative of a first portion of the story. A first acoustic input is received and a speech recognition engine is employed to recognize the first acoustic input. An output of the speech recognition engine is compared to the language model and a signal indicative of whether the output of the speech recognition matches at least a portion of the target context free grammar is provided.
摘要翻译：公开了提供自动阅读辅导的方法。该方法包括从数据存储中检索故事的文本指示并创建包括构建指示故事的第一部分的目标上下文自由语法的语言模型。接收到第一声输入，并且采用语音识别引擎来识别第一声输入。将语音识别引擎的输出与语言模型进行比较，并且提供指示语音识别的输出是否与目标语境自由语法的至少一部分匹配的信号。

19. 发明授权

US07454336B2 Variational inference and learning for segmental switching state space models of hidden speech dynamics 有权
标题翻译：隐性语音动力学的分段切换状态空间模型的变分推理和学习
公开(公告)号：US07454336B2
公开(公告)日：2008-11-18
申请号：US10600798
申请日：2003-06-20
申请人： Hagai Attias , Li Deng , Leo J. Lee
发明人： Hagai Attias , Li Deng , Leo J. Lee
IPC分类号： G10L15/00 , G10L15/28 , G10L15/14
CPC分类号： G06K9/6226 , G10L15/063 , G10L15/144
摘要： A system and method that facilitate modeling unobserved speech dynamics based upon a hidden dynamic speech model in the form of segmental switching state space model that employs model parameters including those describing the unobserved speech dynamics and those describing the relationship between the unobserved speech dynamic vector and the observed acoustic feature vector is provided. The model parameters are modified based, at least in part, upon, a variational learning technique. In accordance with an aspect of the present invention, novel and powerful variational expectation maximization (EM) algorithm(s) for the segmental switching state space models used in speech applications, which are capable of capturing key internal (or hidden) dynamics of natural speech production, are provided. For example, modification of model parameters can be based upon an approximate mixture of Gaussian (MOG) posterior and/or based upon an approximate hidden Markov model (HMM) posterior using a variational technique.
摘要翻译：一种以分段切换状态空间模型为基础的隐藏动态语音模型，建立不可观察到的语音动力学的系统和方法，该模型采用模型参数，包括描述不可观察语言动力学的模型参数，以及描述未观察语言动态向量与提供观察到的声学特征向量。模型参数至少部分地基于变分学习技术进行修改。根据本发明的一个方面，用于语音应用中使用的分段切换状态空间模型的新颖和强大的变分期望最大化（EM）算法，其能够捕获自然语音的关键内部（或隐藏）动态生产，提供。例如，模型参数的修改可以基于高斯（MOG）后验和/或基于使用变分技术的近似隐马尔科夫模型（HMM））的近似混合。

20. 发明授权

US07447630B2 Method and apparatus for multi-sensory speech enhancement 有权
标题翻译：多感官语音增强的方法和装置
公开(公告)号：US07447630B2
公开(公告)日：2008-11-04
申请号：US10724008
申请日：2003-11-26
申请人： Zicheng Liu , Michael J. Sinclair , Alejandro Acero , Xuedong D. Huang , James G. Droppo , Li Deng , Zhengyou Zhang , Yanli Zheng
发明人： Zicheng Liu , Michael J. Sinclair , Alejandro Acero , Xuedong D. Huang , James G. Droppo , Li Deng , Zhengyou Zhang , Yanli Zheng
IPC分类号： G10L21/02
CPC分类号： G10L21/0208 , G10L2021/02165
摘要： A method and system use an alternative sensor signal received from a sensor other than an air conduction microphone to estimate a clean speech value. The estimation uses either the alternative sensor signal alone, or in conjunction with the air conduction microphone signal. The clean speech value is estimated without using a model trained from noisy training data collected from an air conduction microphone. Under one embodiment, correction vectors are added to a vector formed from the alternative sensor signal in order to form a filter, which is applied to the air conductive microphone signal to produce the clean speech estimate. In other embodiments, the pitch of a speech signal is determined from the alternative sensor signal and is used to decompose an air conduction microphone signal. The decomposed signal is then used to determine a clean signal estimate.
摘要翻译：一种方法和系统使用从除空气传导麦克风以外的传感器接收的替代传感器信号来估计干净的语音值。该估计单独使用替代传感器信号，或者与导气麦克风信号一起使用。无需使用从空气传导麦克风收集的噪声训练数据训练的模型来估计干净的语音值。在一个实施例中，校正矢量被添加到由替代传感器信号形成的矢量中，以形成滤波器，该滤波器被施加到空气传导麦克风信号以产生干净的语音估计。在其他实施例中，语音信号的音调由替代传感器信号确定，并用于分解空气传导麦克风信号。然后使用分解的信号来确定干净的信号估计。

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