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1. 发明申请

US20060100862A1 Acoustic models with structured hidden dynamics with integration over many possible hidden trajectories 有权
公开(公告)号：US20060100862A1
公开(公告)日：2006-05-11
申请号：US11071904
申请日：2005-03-01
申请人： Li Deng , Alejandro Acero , Dong Yu , Xiang Li
发明人： Li Deng , Alejandro Acero , Dong Yu , Xiang Li
IPC分类号： G10L11/04
CPC分类号： G10L15/02 , G10L2015/025
摘要： A method of producing at least one possible sequence of vocal tract resonance (VTR) for a fixed sequence of phonetic units, and producing the acoustic observation probability by integrating over such distributions is provided. The method includes identifying a sequence of target distributions for a VTR sequence corresponding to a phone sequence with a given segmentation. The sequence of target distributions is applied to a finite impulse response filter to produce distributions for possible VTR trajectories. Then these distributions are applied to a linearized nonlinear function to produce the acoustic observation probability for the given sequence of phonetic units. This acoustic observation probability is used for phonetic recognition.

2. 发明授权

US07565284B2 Acoustic models with structured hidden dynamics with integration over many possible hidden trajectories 有权
标题翻译：具有结构化隐藏动力学的声学模型，并集成了许多可能的隐藏轨迹
公开(公告)号：US07565284B2
公开(公告)日：2009-07-21
申请号：US11071904
申请日：2005-03-01
申请人： Li Deng , Alejandro Acero , Dong Yu , Xiang Li
发明人： Li Deng , Alejandro Acero , Dong Yu , Xiang Li
IPC分类号： G10L15/14
CPC分类号： G10L15/02 , G10L2015/025
摘要： A method of producing at least one possible sequence of vocal tract resonance (VTR) for a fixed sequence of phonetic units, and producing the acoustic observation probability by integrating over such distributions is provided. The method includes identifying a sequence of target distributions for a VTR sequence corresponding to a phone sequence with a given segmentation. The sequence of target distributions is applied to a finite impulse response filter to produce distributions for possible VTR trajectories. Then these distributions are applied to a linearized nonlinear function to produce the acoustic observation probability for the given sequence of phonetic units. This acoustic observation probability is used for phonetic recognition.
摘要翻译：提供了一种用于固定语音单元序列的至少一个可能的声道共振（VTR）序列的方法，并且通过在这样的分布上积分来产生声学观察概率。该方法包括识别对应于具有给定分割的电话序列的VTR序列的目标分布序列。将目标分布的序列应用于有限脉冲响应滤波器，以产生可能的VTR轨迹的分布。然后将这些分布应用于线性化非线性函数，以产生给定的语音单元序列的声学观察概率。这种声学观测概率用于语音识别。

3. 发明授权

US08214215B2 Phase sensitive model adaptation for noisy speech recognition 有权
标题翻译：嘈杂语音识别的相敏模型适应
公开(公告)号：US08214215B2
公开(公告)日：2012-07-03
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： G10L15/14
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。

4. 发明授权

US08160878B2 Piecewise-based variable-parameter Hidden Markov Models and the training thereof 有权
标题翻译：基于分段的可变参数隐马尔科夫模型及其训练
公开(公告)号：US08160878B2
公开(公告)日：2012-04-17
申请号：US12211114
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 , G10L15/20
CPC分类号： G10L15/144
摘要： A speech recognition system uses Gaussian mixture variable-parameter hidden Markov models (VPHMMs) to recognize speech under many different conditions. Each Gaussian mixture component of the VPHMMs is characterized by a mean parameter μ and a variance parameter Σ. Each of these Gaussian parameters varies as a function of at least one environmental conditioning parameter, such as, but not limited to, instantaneous signal-to-noise-ratio (SNR). The way in which a Gaussian parameter varies with the environmental conditioning parameter(s) can be approximated as a piecewise function, such as a cubic spline function. Further, the recognition system formulates the mean parameter μ and the variance parameter Σ of each Gaussian mixture component in an efficient form that accommodates the use of discriminative training and parameter sharing. Parameter sharing is carried out so that the otherwise very large number of parameters in the VPHMMs can be effectively reduced with practically feasible amounts of training data.
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）来识别许多不同条件下的语音。 VPHMM的每个高斯混合分量的特征在于平均参数μ和方差参数＆Sgr。这些高斯参数中的每一个作为至少一个环境调节参数的函数而变化，例如但不限于瞬时信噪比（SNR）。高斯参数随环境条件参数变化的方式可以近似为分段函数，如三次样条函数。此外，识别系统制定均值参数μ和方差参数＆Sgr; 每个高斯混合分量以有效的形式适应使用歧视性训练和参数共享。执行参数共享，以便通过实际可行的训练数据量可以有效地减少VPHMM中非常大量的参数。

5. 发明授权

US08145488B2 Parameter clustering and sharing for variable-parameter hidden markov models 有权
标题翻译：可变参数隐马尔可夫模型的参数聚类和共享
公开(公告)号：US08145488B2
公开(公告)日：2012-03-27
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。

6. 发明授权

US07734460B2 Time asynchronous decoding for long-span trajectory model 失效
标题翻译：用于长跨度轨迹模型的时间异步解码
公开(公告)号：US07734460B2
公开(公告)日：2010-06-08
申请号：US11311951
申请日：2005-12-20
申请人： Dong Yu , Li Deng , Alejandro Acero
发明人： Dong Yu , Li Deng , Alejandro Acero
IPC分类号： G06F17/21 , G06F17/27 , G10L15/00
CPC分类号： G10L15/08 , G10L15/187
摘要： A time-asynchronous lattice-constrained search algorithm is developed and used to process a linguistic model of speech that has a long-contextual-span capability. In the algorithm, nodes and links in the lattices developed from the model are expanded via look-ahead. Heuristics as utilized by a search algorithm are estimated. Additionally, pruning strategies can be applied to speed up the search.
摘要翻译：开发了时间异步网格约束搜索算法，用于处理具有长语境跨度能力的语言语言模型。在算法中，从模型开发的网格中的节点和链接通过预先扩展。估计搜索算法使用的启发式算法。此外，可以应用修剪策略来加快搜索速度。

7. 发明授权

US07565292B2 Quantitative model for formant dynamics and contextually assimilated reduction in fluent speech 有权
标题翻译：流动语言的共同作用动力学和语境相似化减少的量化模型
公开(公告)号：US07565292B2
公开(公告)日：2009-07-21
申请号：US10944262
申请日：2004-09-17
申请人： Li Deng , Alejandro Acero , Dong Yu
发明人： Li Deng , Alejandro Acero , Dong Yu
IPC分类号： G10L13/00
CPC分类号： G10L13/02 , G10L25/15
摘要： A method of identifying a sequence of formant trajectory values is provided in which a sequence of target values are identified for a formant as step functions. The target values and the duration for each segment target for the formant are applied to a finite impulse response filter to form a sequence of formant trajectory values. The parameters of this filter, as well as the duration of the targets for each phone, can be modified to produce many kinds of target undershooting effects in a contextually assimilated manner. The procedure for producing the formant trajectory values does not require any acoustic data from speech.
摘要翻译：提供了一种识别共振峰轨迹值序列的方法，其中以阶跃函数为共振峰识别目标值序列。将目标值和共振峰的每个段目标的持续时间应用于有限脉冲响应滤波器以形成共振峰轨迹值序列。可以修改此过滤器的参数以及每个手机的目标持续时间，以上下文相同的方式产生多种目标下冲效应。用于产生共振峰轨迹值的过程不需要来自语音的任何声学数据。

8. 发明申请

US20060229875A1 Speaker adaptive learning of resonance targets in a hidden trajectory model of speech coarticulation 有权
公开(公告)号：US20060229875A1
公开(公告)日：2006-10-12
申请号：US11093833
申请日：2005-03-30
申请人： Alejandro Acero , Dong Yu , Li Deng
发明人： Alejandro Acero , Dong Yu , Li Deng
IPC分类号： G10L13/00
CPC分类号： G10L15/07 , G10L2015/0638
摘要： A computer-implemented method is provided for training a hidden trajectory model, of a speech recognition system, which generates Vocal Tract Resonance (VTR) targets. The method includes obtaining generic VTR target parameters corresponding to a generic speaker used by a target selector to generate VTR target sequences. The generic VTR target parameters are scaled for a particular speaker using a speaker-dependent scaling factor for the particular speaker to generate speaker-adaptive VTR target parameters. This scaling is performed for both the training data and the test data, and for the training data, the scaling is performed iteratively with the process of obtaining the generic targets. The computation of the scaling factor makes use of the results of a VTR tracker. The speaker-adaptive VTR target parameters for the particular speaker are then stored in order to configure the hidden trajectory model to perform speech recognition for the particular speaker using the speaker-adaptive VTR target parameters.

9. 发明申请

US20120254086A1 DEEP CONVEX NETWORK WITH JOINT USE OF NONLINEAR RANDOM PROJECTION, RESTRICTED BOLTZMANN MACHINE AND BATCH-BASED PARALLELIZABLE OPTIMIZATION 有权
标题翻译：连续使用非线性随机投影，限制性BOLTZMANN机器和基于批量的平行优化的深层网络
公开(公告)号：US20120254086A1
公开(公告)日：2012-10-04
申请号：US13077978
申请日：2011-03-31
申请人： Li Deng , Dong Yu , Alejandro Acero
发明人： Li Deng , Dong Yu , Alejandro Acero
IPC分类号： G06N3/08
CPC分类号： G06N3/08 , G06N3/02 , G06N3/04 , G06N3/0454
摘要： A method is disclosed herein that includes an act of causing a processor to access a deep-structured, layered or hierarchical model, called deep convex network, retained in a computer-readable medium, wherein the deep-structured model comprises a plurality of layers with weights assigned thereto. This layered model can produce the output serving as the scores to combine with transition probabilities between states in a hidden Markov model and language model scores to form a full speech recognizer. The method makes joint use of nonlinear random projections and RBM weights, and it stacks a lower module's output with the raw data to establish its immediately higher module. Batch-based, convex optimization is performed to learn a portion of the deep convex network's weights, rendering it appropriate for parallel computation to accomplish the training. The method can further include the act of jointly substantially optimizing the weights, the transition probabilities, and the language model scores of the deep-structured model using the optimization criterion based on a sequence rather than a set of unrelated frames.
摘要翻译：本文公开了一种方法，其包括使处理器访问被保留在计算机可读介质中的称为深凸网络的深层结构的分层或层次模型的动作，其中深层结构模型包括多个具有分配给它的权重。该分层模型可以产生作为分数的输出，以与隐藏的马尔可夫模型和语言模型分数中的状态之间的转移概率相结合，以形成完整的语音识别器。该方法联合使用非线性随机投影和RBM权重，并将较低模块的输出与原始数据叠加以建立其立即更高的模块。执行基于批次的凸优化来学习深凸网络权重的一部分，使其适合于并行计算以完成训练。该方法还可以包括使用基于序列而不是一组不相关帧的优化准则共同基本优化深层结构模型的权重，转移概率和语言模型分数的动作。

10. 发明授权

US08185389B2 Noise suppressor for robust speech recognition 有权
标题翻译：噪声抑制器用于强大的语音识别
公开(公告)号：US08185389B2
公开(公告)日：2012-05-22
申请号：US12335558
申请日：2008-12-16
申请人： Dong Yu , Li Deng , Yifan Gong , Jian Wu , Alejandro Acero
发明人： Dong Yu , Li Deng , Yifan Gong , Jian Wu , Alejandro Acero
IPC分类号： G10L15/20
CPC分类号： G10L21/0208 , G10L15/20
摘要： Described is noise reduction technology generally for speech input in which a noise-suppression related gain value for the frame is determined based upon a noise level associated with that frame in addition to the signal to noise ratios (SNRs). In one implementation, a noise reduction mechanism is based upon minimum mean square error, Mel-frequency cepstra noise reduction technology. A high gain value (e.g., one) is set to accomplish little or no noise suppression when the noise level is below a threshold low level, and a low gain value set or computed to accomplish large noise suppression above a threshold high noise level. A noise-power dependent function, e.g., a log-linear interpolation, is used to compute the gain between the thresholds. Smoothing may be performed by modifying the gain value based upon a prior frame's gain value. Also described is learning parameters used in noise reduction via a step-adaptive discriminative learning algorithm.
摘要翻译：描述了通常用于语音输入的噪声降低技术，其中除了信噪比（SNR）之外，基于与该帧相关联的噪声电平来确定用于帧的噪声抑制相关增益值。在一个实现中，降噪机制基于最小均方误差，Mel-frequency cepstra降噪技术。设置高增益值（例如一个），以在噪声电平低于阈值低电平时实现很少或没有噪声抑制，以及设置或计算的低增益值，以实现高于阈值高噪声电平的大噪声抑制。使用噪声功率相关函数，例如对数线性插值来计算阈值之间的增益。可以通过基于先前帧的增益值修改增益值来执行平滑化。还描述了通过步进自适应识别学习算法在降噪中使用的学习参数。

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