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1. 发明申请

US20050259558A1 Noise reduction using correction vectors based on dynamic aspects of speech and noise normalization 有权
标题翻译：基于语音和噪声归一化的动态方面的校正矢量降噪
公开(公告)号：US20050259558A1
公开(公告)日：2005-11-24
申请号：US11189974
申请日：2005-07-26
申请人： James Droppo , Li Deng , Alejandro Acero
发明人： James Droppo , Li Deng , Alejandro Acero
IPC分类号： G10L21/02 , G11B7/00 , G11B7/24
CPC分类号： G10L21/0208
摘要： A method and apparatus are provided for reducing noise in a signal. Under one aspect of the invention, a correction vector is selected based on a noisy feature vector that represents a noisy signal. The selected correction vector incorporates dynamic aspects of pattern signals. The selected correction vector is then added to the noisy feature vector to produce a cleaned feature vector. In other aspects of the invention, a noise value is produced from an estimate of the noise in a noisy signal. The noise value is subtracted from a value representing a portion of the noisy signal to produce a noise-normalized value. The noise-normalized value is used to select a correction value that is added to the noise-normalized value to produce a cleaned noise-normalized value. The noise value is then added to the cleaned noise-normalized value to produce a cleaned value representing a portion of a cleaned signal.
摘要翻译：提供了一种降低信号噪声的方法和装置。在本发明的一个方面，基于表示噪声信号的噪声特征向量来选择校正矢量。所选择的校正矢量包含模式信号的动态方面。然后将所选择的校正向量加到噪声特征向量中以产生清除的特征向量。在本发明的其他方面，噪声值是由噪声信号中的噪声的估计产生的。从表示噪声信号的一部分的值中减去噪声值，以产生噪声归一化值。噪声归一化值用于选择加到噪声归一化值的校正值以产生清洁的噪声归一化值。然后将噪声值添加到清洁的噪声归一化值，以产生表示清洁信号的一部分的清洁值。

2. 发明申请

US20060206325A1 Method of pattern recognition using noise reduction uncertainty 有权
公开(公告)号：US20060206325A1
公开(公告)日：2006-09-14
申请号：US11435254
申请日：2006-05-16
申请人： James Droppo , Alejandro Acero , Li Deng
发明人： James Droppo , Alejandro Acero , Li Deng
IPC分类号： G10L15/20
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.

3. 发明申请

US20060206322A1 Method of noise reduction based on dynamic aspects of speech 有权
公开(公告)号：US20060206322A1
公开(公告)日：2006-09-14
申请号：US11433873
申请日：2006-05-12
申请人： Li Deng , James Droppo , Alejandro Acero
发明人： Li Deng , James Droppo , Alejandro Acero
IPC分类号： G10L21/02
CPC分类号： G10L21/0208 , G10L15/20
摘要： A system and method are provided that reduce noise in pattern recognition signals. To do this, embodiments of the present invention utilize a prior model of dynamic aspects of clean speech together with one or both of a prior model of static aspects of clean speech, and an acoustic model that indicates the relationship between clean speech, noisy speech and noise. In one embodiment, components of a noise-reduced feature vector are produced by forming a weighted sum of predicted values from the prior model of dynamic aspects of clean speech, the prior model of static aspects of clean speech and the acoustic-environmental model.

4. 发明申请

US20050114124A1 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：US20050114124A1
公开(公告)日：2005-05-26
申请号：US10724008
申请日：2003-11-26
申请人： Zicheng Liu , Michael Sinclair , Alejandro Acero , Xuedong Huang , James Droppo , Li Deng , Zhengyou Zhang , Yanli Zheng
发明人： Zicheng Liu , Michael Sinclair , Alejandro Acero , Xuedong Huang , James 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。

5. 发明申请

US20050071157A1 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：US20050071157A1
公开(公告)日：2005-03-31
申请号：US10985896
申请日：2004-11-10
申请人： James Droppo , Alejandro Acero , Li Deng
发明人： James Droppo , Alejandro Acero , Li Deng
IPC分类号： G10L15/20 , G10L21/02 , G10L21/00
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。

6. 发明申请

US20070106504A1 Method of determining uncertainty associated with acoustic distortion-based noise reduction 有权
标题翻译：确定与基于声学失真的噪声降低有关的方法
公开(公告)号：US20070106504A1
公开(公告)日：2007-05-10
申请号：US11642389
申请日：2006-12-20
申请人： Li Deng , Alejandro Acero , James Droppo
发明人： Li Deng , Alejandro Acero , James Droppo
IPC分类号： 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.
摘要翻译：提供了一种基于语音失真的参数模型来确定降噪中的不确定性的方法和装置。该方法首先用于降低噪声信号中的噪声。特别地，从噪声信号的一部分的表示中减少噪声，以通过利用声学环境模型来产生清洁信号的表示。然后计算与降噪过程相关的不确定性。在一个实施例中，噪声降低处理的不确定性结合降噪信号被用于解码图案状态。

7. 发明申请

US20060206321A1 Noise reduction using correction vectors based on dynamic aspects of speech and noise normalization 有权
公开(公告)号：US20060206321A1
公开(公告)日：2006-09-14
申请号：US11429630
申请日：2006-05-05
申请人： James Droppo , Li Deng , Alejandro Acero
发明人： James Droppo , Li Deng , Alejandro Acero
IPC分类号： G10L21/02
CPC分类号： G10L21/0208
摘要： A method and apparatus are provided for reducing noise in a signal. Under one aspect of the invention, a correction vector is selected based on a noisy feature vector that represents a noisy signal. The selected correction vector incorporates dynamic aspects of pattern signals. The selected correction vector is then added to the noisy feature vector to produce a cleaned feature vector. In other aspects of the invention, a noise value is produced from an estimate of the noise in a noisy signal. The noise value is subtracted from a value representing a portion of the noisy signal to produce a noise-normalized value. The noise-normalized value is used to select a correction value that is added to the noise-normalized value to produce a cleaned noise-normalized value. The noise value is then added to the cleaned noise-normalized value to produce a cleaned value representing a portion of a cleaned signal.

8. 发明申请

US20050182624A1 Method and apparatus for constructing a speech filter using estimates of clean speech and noise 有权
标题翻译：用于使用干净的语音和噪声的估计来构造语音滤波器的方法和装置
公开(公告)号：US20050182624A1
公开(公告)日：2005-08-18
申请号：US10780177
申请日：2004-02-16
申请人： Jian Wu , James Droppo , Li Deng , Alejandro Acero
发明人： Jian Wu , James Droppo , Li Deng , Alejandro Acero
IPC分类号： G10L21/00 , G10L21/02
CPC分类号： G10L21/0208
摘要： A method and apparatus identify a clean speech signal from a noisy speech signal. To do this, a clean speech value and a noise value are estimated from the noisy speech signal. The clean speech value and the noise value are then used to define a gain on a filter. The noisy speech signal is applied to the filter to produce the clean speech signal. Under some embodiments, the noise value and the clean speech value are used in both the numerator and the denominator of the filter gain, with the numerator being guaranteed to be positive.
摘要翻译：方法和装置从噪声语音信号中识别干净的语音信号。为此，从噪声语音信号估计干净的语音值和噪声值。然后使用干净的语音值和噪声值来定义滤波器上的增益。噪声语音信号被施加到滤波器以产生干净的语音信号。在一些实施例中，噪声值和清洁语音值用于滤波器增益的分子和分母，分子保证为正。

9. 发明申请

US20050043945A1 Method of noise reduction using instantaneous signal-to-noise ratio as the principal quantity for optimal estimation 有权
标题翻译：使用瞬时信噪比作为最优估计的主要数量的降噪方法
公开(公告)号：US20050043945A1
公开(公告)日：2005-02-24
申请号：US10643370
申请日：2003-08-19
申请人： James Droppo , Li Deng , Alejandro Acero
发明人： James Droppo , Li Deng , Alejandro Acero
IPC分类号： G10L15/20 , G10L21/00
CPC分类号： G10L21/0208
摘要： A system and method are provided that accurately estimate noise and that reduce noise in pattern recognition signals. The method and system define a mapping random variable as a function of at least a clean signal random variable and a noise random variable. A model parameter that describes at least one aspect of a distribution of values for the mapping random variable is then determined. Based on the model parameter, an estimate for the clean signal random variable is determined. Under many aspects of the present invention, the mapping random variable is a signal-to-noise ratio variable and the method and system estimate a value for the signal-to-noise ratio variable from the model parameter.
摘要翻译：提供了一种准确估计噪声并降低模式识别信号噪声的系统和方法。该方法和系统将映射随机变量定义为至少一个干净的信号随机变量和噪声随机变量的函数。然后确定描述映射随机变量的值的分布的至少一个方面的模型参数。基于模型参数，确定干净信号随机变量的估计。在本发明的许多方面，映射随机变量是信噪比变量，并且该方法和系统根据模型参数估计信噪比变量的值。

10. 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。

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