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71. 发明授权

US07617098B2 Method of noise reduction based on dynamic aspects of speech 有权
标题翻译：基于语音动态方面的降噪方法
公开(公告)号：US07617098B2
公开(公告)日：2009-11-10
申请号：US11433873
申请日：2006-05-12
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 , G10L15/20
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.
摘要翻译：提供了减少模式识别信号中的噪声的系统和方法。为此，本发明的实施例利用干净语音的动态方面的先前模型以及干净语音的静态方面的先前模型中的一个或两者，以及指示清洁语音，噪声语音和噪声。在一个实施例中，噪声降低的特征向量的分量通过从干涉语音的动态方面的先前模型，干净语音的静态方面的先前模型和声环境模型形成预测值的加权和来产生。

72. 发明申请

US20090177468A1 SPEECH RECOGNITION WITH NON-LINEAR NOISE REDUCTION ON MEL-FREQUENCY CEPTRA 有权
标题翻译：语音识别与非线性噪声减少在频率CEPTRA
公开(公告)号：US20090177468A1
公开(公告)日：2009-07-09
申请号：US11970537
申请日：2008-01-08
申请人： Dong Yu , Alejandro Acero , James G. Droppo , Li Deng
发明人： Dong Yu , Alejandro Acero , James G. Droppo , Li Deng
IPC分类号： G10L15/20
CPC分类号： G10L15/20 , G10L15/02 , G10L21/02 , G10L25/24
摘要： In an automatic speech recognition system, a feature extractor extracts features from a speech signal, and speech is recognized by the automatic speech recognition system based on the extracted features. Noise reduction as part of the feature extractor is provided by feature enhancement in which feature-domain noise reduction in the form of Mel-frequency cepstra is provided based on the minimum means square error criterion. Specifically, the devised method takes into account the random phase between the clean speech and the mixing noise. The feature-domain noise reduction is performed in a dimension-wise fashion to the individual dimensions of the feature vectors input to the automatic speech recognition system, in order to perform environment-robust speech recognition.
摘要翻译：在自动语音识别系统中，特征提取器从语音信号中提取特征，并且基于提取的特征，通过自动语音识别系统识别语音。通过特征增强提供降噪作为特征提取器的一部分，其中基于最小均方误差准则提供了以Mel-frequency cepstra形式的特征域降噪。具体来说，设计的方法考虑了清洁语音和混合噪声之间的随机相位。为了执行环境鲁棒的语音识别，特征域噪声降低以维度方式执行到输入到自动语音识别系统的特征向量的各个维度。

73. 发明授权

US07519531B2 Speaker adaptive learning of resonance targets in a hidden trajectory model of speech coarticulation 有权
标题翻译：演讲者自适应学习的共振目标在隐藏轨迹模型的语音合成
公开(公告)号：US07519531B2
公开(公告)日：2009-04-14
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： G10L19/06
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.
摘要翻译：提供了一种计算机实现的方法，用于训练产生声音轨道共振（VTR）目标的语音识别系统的隐藏轨迹模型。该方法包括获得对应于由目标选择器使用的通用扬声器生成VTR目标序列的通用VTR目标参数。使用与特定扬声器相关的扬声器相关的缩放因子来为特定扬声器对通用VTR目标参数进行缩放以产生说话者自适应VTR目标参数。对训练数据和测试数据进行该缩放，对于训练数据，通过获得通用目标的过程迭代地执行缩放。缩放因子的计算使用VTR跟踪器的结果。然后存储用于特定扬声器的扬声器自适应VTR目标参数，以便配置隐藏轨迹模型，以使用扬声器自适应VTR目标参数为特定扬声器执行语音识别。

74. 发明申请

US20080281591A1 METHOD OF PATTERN RECOGNITION USING NOISE REDUCTION UNCERTAINTY 有权
标题翻译：使用噪声减少不确定度的图案识别方法
公开(公告)号：US20080281591A1
公开(公告)日：2008-11-13
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
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。

75. 发明申请

US20080201139A1 Generic framework for large-margin MCE training in speech recognition 有权
标题翻译：语言识别中大面积MCE培训的通用框架
公开(公告)号：US20080201139A1
公开(公告)日：2008-08-21
申请号：US11708440
申请日：2007-02-20
申请人： Dong Yu , Alejandro Acero , Li Deng , Xiaodong He
发明人： Dong Yu , Alejandro Acero , Li Deng , Xiaodong He
IPC分类号： G10L15/00
CPC分类号： G10L15/063 , G10L2015/0631
摘要： A method and apparatus for training an acoustic model are disclosed. A training corpus is accessed and converted into an initial acoustic model. Scores are calculated for a correct class and competitive classes, respectively, for each token given the initial acoustic model. Also, a sample-adaptive window bandwidth is calculated for each training token. From the calculated scores and the sample-adaptive window bandwidth values, loss values are calculated based on a loss function. The loss function, which may be derived from a Bayesian risk minimization viewpoint, can include a margin value that moves a decision boundary such that token-to-boundary distances for correct tokens that are near the decision boundary are maximized. The margin can either be a fixed margin or can vary monotonically as a function of algorithm iterations. The acoustic model is updated based on the calculated loss values. This process can be repeated until an empirical convergence is met.
摘要翻译：公开了一种用于训练声学模型的方法和装置。训练语料库被访问并转换成初始声学模型。对于给定初始声学模型的每个令牌，分数计算分别为正确的类和竞争类。此外，针对每个训练令牌计算样本自适应窗口带宽。从计算出的分数和采样自适应窗口带宽值，根据损失函数计算损失值。可以从贝叶斯风险最小化观点导出的损失函数可以包括移动判定边界的边距值，使得靠近判定边界的正确令牌的令牌到边界的距离最大化。边距可以是固定边距，也可以作为算法迭代的函数单调变化。基于计算的损失值更新声学模型。可以重复该过程，直到满足经验收敛。

76. 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.

77. 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。

78. 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.

79. 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。

80. 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。

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