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51. 发明授权

US07289955B2 Method of determining uncertainty associated with acoustic distortion-based noise reduction 有权
公开(公告)号：US07289955B2
公开(公告)日：2007-10-30
申请号：US11642389
申请日：2006-12-20
申请人： Li Deng , Alejandro Acero , James G. Droppo
发明人： Li Deng , Alejandro Acero , James G. Droppo
IPC分类号： G10L15/20 , G10L21/02
CPC分类号： G10L15/20 , G10L21/0208
摘要： A method and apparatus are provided for determining uncertainty in noise reduction based on a parametric model of speech distortion. The method is first used to reduce noise in a noisy signal. In particular, noise is reduced from a representation of a portion of a noisy signal to produce a representation of a cleaned signal by utilizing an acoustic environment model. The uncertainty associated with the noise reduction process is then computed. In one embodiment, the uncertainty of the noise reduction process is used, in conjunction with the noise-reduced signal, to decode a pattern state.

52. 发明授权

US07266494B2 Method and apparatus for identifying noise environments from noisy signals 有权
标题翻译：用于从噪声信号中识别噪声环境的方法和装置
公开(公告)号：US07266494B2
公开(公告)日：2007-09-04
申请号：US10985896
申请日：2004-11-10
申请人： James G. Droppo , Alejandro Acero , Li Deng
发明人： James G. Droppo , Alejandro Acero , Li Deng
IPC分类号： G10L21/02
CPC分类号： G10L21/0208 , G10L15/20 , G10L21/0216
摘要： A method and apparatus are provided for identifying a noise environment for a frame of an input signal based on at least one feature for that frame. To identify the noise environment, a probability for a noise environment is determined by applying the noisy input feature vector to a distribution of noisy training feature vectors. In one embodiment, each noisy training feature vector in the distribution is formed by modifying a set of clean training feature vectors. In one embodiment, the probabilities of the noise environments for past frames are included in the identification of an environment for a current frame. In one embodiment, a correction vector is then selected based on the identified noise environment.
摘要翻译：提供了一种方法和装置，用于基于该帧的至少一个特征来识别输入信号的帧的噪声环境。为了识别噪声环境，通过将噪声输入特征向量应用于噪声训练特征向量的分布来确定噪声环境的概率。在一个实施例中，通过修改一组干净的训练特征向量来形成分布中的每个噪声训练特征向量。在一个实施例中，过去帧的噪声环境的概率被包括在当前帧的环境的识别中。在一个实施例中，然后基于所识别的噪声环境来选择校正矢量。

53. 发明授权

US07139703B2 Method of iterative noise estimation in a recursive framework 有权
公开(公告)号：US07139703B2
公开(公告)日：2006-11-21
申请号：US10237162
申请日：2002-09-06
申请人： Alejandro Acero , Li Deng , James G. Droppo
发明人： Alejandro Acero , Li Deng , James G. Droppo
IPC分类号： G10L21/00 , G10L21/02
CPC分类号： G10L21/02 , G10L21/0208 , G10L21/0216
摘要： A method and apparatus estimate additive noise in a noisy signal using an iterative technique within a recursive framework. In particular, the noisy signal is divided into frames and the noise in each frame is determined based on the noise in another frame and the noise determined in a previous iteration for the current frame. In one particular embodiment, the noise found in a previous iteration for a frame is used to define an expansion point for a Taylor series approximation that is used to estimate the noise in the current frame. In one embodiment, noise estimation employs a recursive-Expectation-Maximization framework with a maximum likelihood (ML) criteria. In a further embodiment, noise estimation employs a recursive-Expectation-Maximization framework based on a MAP (maximum a posterior) criteria.

54. 发明授权

US07047047B2 Non-linear observation model for removing noise from corrupted signals 有权
标题翻译：用于从损坏的信号中去除噪声的非线性观测模型
公开(公告)号：US07047047B2
公开(公告)日：2006-05-16
申请号：US10237163
申请日：2002-09-06
申请人： Alejandro Acero , Li Deng , James G. Droppo
发明人： Alejandro Acero , Li Deng , James G. Droppo
IPC分类号： H04B1/38
CPC分类号： G10L21/0208 , G10L15/20 , G10L21/0216
摘要： A new statistical model describes the corruption of spectral features caused by additive noise. In particular, the model explicitly represents the effect of unknown phase together with the unobserved clean signal and noise. Development of the model has realized three techniques for reducing noise in a noisy signal as a function of the model.
摘要翻译：一个新的统计模型描述了由加性噪声引起的光谱特征的破坏。特别地，该模型明确地表示未知相的影响以及未观察到的清洁信号和噪声。该模型的开发实现了三种降低噪声信号噪声的技术，作为模型的一个功能。

55. 发明授权

US06990447B2 Method and apparatus for denoising and deverberation using variational inference and strong speech models 有权
公开(公告)号：US06990447B2
公开(公告)日：2006-01-24
申请号：US09999576
申请日：2001-11-15
申请人： Hagai Attias , John Carlton Platt , Li Deng , Alejandro Acero
发明人： Hagai Attias , John Carlton Platt , Li Deng , Alejandro Acero
IPC分类号： G10L15/08 , G10L15/12 , G10L15/06 , G10L21/02
CPC分类号： G10L21/0208 , G10L2021/02082 , H04R2225/43
摘要： A probability distribution for speech model parameters, such as auto-regression parameters, is used to identify a distribution of denoised values from a noisy signal. Under one embodiment, the probability distributions of the speech model parameters and the denoised values are adjusted to improve a variational inference so that the variational inference better approximates the joint probability of the speech model parameters and the denoised values given a noisy signal. In some embodiments, this improvement is performed during an expectation step in an expectation-maximization algorithm. The statistical model can also be used to identify an average spectrum for the clean signal and this average spectrum may be provided to a speech recognizer instead of the estimate of the clean signal.

56. 发明授权

US06944590B2 Method of iterative noise estimation in a recursive framework 失效
标题翻译：递归框架中迭代噪声估计的方法
公开(公告)号：US06944590B2
公开(公告)日：2005-09-13
申请号：US10116792
申请日：2002-04-05
申请人： Li Deng , James G. Droppo , Alejandro Acero
发明人： Li Deng , James G. Droppo , Alejandro Acero
IPC分类号： G10L21/02
CPC分类号： G10L21/0208 , G10L21/0216
摘要： A method and apparatus estimate additive noise in a noisy signal using an iterative technique within a recursive framework. In particular, the noisy signal is divided into frames and the noise in each frame is determined based on the noise in another frame and the noise determined in a previous iteration for the current frame. In one particular embodiment, the noise found in a previous iteration for a frame is used to define an expansion point for a Taylor series approximation that is used to estimate the noise in the current frame.
摘要翻译：方法和装置使用递归框架内的迭代技术来估计噪声信号中的加性噪声。特别地，噪声信号被划分为帧，并且基于另一帧中的噪声和当前帧的先前迭代中确定的噪声来确定每帧中的噪声。在一个特定实施例中，在先前的帧迭代中发现的噪声用于定义用于估计当前帧中的噪声的泰勒级数近似的扩展点。

57. 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。

58. 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。

59. 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。

60. 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。

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