公开(公告)号：US20080177545A1

公开(公告)日：2008-07-24

申请号：US11655702

申请日：2007-01-19

申请人： Xiaolong Li ,  Yun-Cheng Ju ,  Li Deng ,  Alejandro Acero

发明人： Xiaolong Li ,  Yun-Cheng Ju ,  Li Deng ,  Alejandro Acero

IPC分类号： G10L15/28

CPC分类号： G06F17/271 ,  G09B17/003 ,  G10L15/197 ,  G10L2015/221

摘要： A novel system for automatic reading tutoring provides effective error detection and reduced false alarms combined with low processing time burdens and response times short enough to maintain a natural, engaging flow of interaction. According to one illustrative embodiment, an automatic reading tutoring method includes displaying a text output and receiving an acoustic input. The acoustic input is modeled with a domain-specific target language model specific to the text output, and with a general-domain garbage language model, both of which may be efficiently constructed as context-free grammars. The domain-specific target language model may be built dynamically or “on-the-fly” based on the currently displayed text (e.g. the story to be read by the user), while the general-domain garbage language model is shared among all different text outputs. User-perceptible tutoring feedback is provided based on the target language model and the garbage language model.

摘要翻译： 用于自动阅读辅导的新颖系统提供了有效的错误检测和减少的假警报以及较短的处理时间负担和响应时间足够短以保持自然的，互动的互动流。 根据一个说明性实施例，自动阅读辅导方法包括显示文本输出并接收声输入。 声输入是用专门针对文本输出的领域特定的目标语言模型建立的，并且具有通用域垃圾语言模型，这两种语言模型都可以被有效地构建为无上下文的语法。 可以基于当前显示的文本（例如，用户要阅读的故事）动态地或“即时”地构建特定领域的目标语言模型，而一般域垃圾语言模型在所有不同的方式之间共享 文本输出。 基于目标语言模型和垃圾语言模型提供了用户可感知的辅导反馈。

公开(公告)号：US07174292B2

公开(公告)日：2007-02-06

申请号：US10236042

申请日：2002-09-05

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.

公开(公告)号：US07003455B1

公开(公告)日：2006-02-21

申请号：US09688764

申请日：2000-10-16

申请人： Li Deng ,  Xuedong Huang ,  Alejandro Acero

发明人： Li Deng ,  Xuedong Huang ,  Alejandro Acero

IPC分类号： G10L15/20

CPC分类号： G10L21/0208

摘要： A method and apparatus are provided for reducing noise in a training signal and/or test signal. The noise reduction technique uses a stereo signal formed of two channel signals, each channel containing the same pattern signal. One of the channel signals is “clean” and the other includes additive noise. Using feature vectors from these channel signals, a collection of noise correction and scaling vectors is determined. When a feature vector of a noisy pattern signal is later received, it is multiplied by the best scaling vector for that feature vector and the best correction vector is added to the product to produce a noise reduced feature vector. Under one embodiment, the best scaling and correction vectors are identified by choosing an optimal mixture component for the noisy feature vector. The optimal mixture component being selected based on a distribution of noisy channel feature vectors associated with each mixture component.

摘要翻译： 提供了一种用于减少训练信号和/或测试信号中的噪声的方法和装置。 噪声降低技术使用由两个信道信号形成的立体声信号，每个信道包含相同的模式信号。 一个通道信号是“干净的”，另一个包括加性噪声。 使用来自这些信道信号的特征向量，确定噪声校正和缩放向量的集合。 当稍后接收到噪声模式信号的特征向量时，将其乘以该特征向量的最佳缩放向量，并将最佳校正向量加到乘积以产生降噪特征向量。 在一个实施例中，通过为噪声特征向量选择最佳混合分量来识别最佳缩放和校正矢量。 基于与每个混合物组分相关联的噪声通道特征向量的分布来选择最佳混合物组分。

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。

公开(公告)号：US20050149325A1

公开(公告)日：2005-07-07

申请号：US11059036

申请日：2005-02-16

申请人： Li Deng ,  Xuedong Huang ,  Alejandro Acero

发明人： Li Deng ,  Xuedong Huang ,  Alejandro Acero

IPC分类号： G10L15/20 ,  G10L21/02 ,  G10L21/00

CPC分类号： G10L21/0208

摘要： A method and apparatus are provided for reducing noise in a training signal and/or test signal. The noise reduction technique uses a stereo signal formed of two channel signals, each channel containing the same pattern signal. One of the channel signals is “clean” and the other includes additive noise. Using feature vectors from these channel signals, a collection of noise correction and scaling vectors is determined. When a feature vector of a noisy pattern signal is later received, it is multiplied by the best scaling vector for that feature vector and the best correction vector is added to the product to produce a noise reduced feature vector. Under one embodiment, the best scaling and correction vectors are identified by choosing an optimal mixture component for the noisy feature vector. The optimal mixture component being selected based on a distribution of noisy channel feature vectors associated with each mixture component.

摘要翻译： 提供了一种用于减少训练信号和/或测试信号中的噪声的方法和装置。 噪声降低技术使用由两个信道信号形成的立体声信号，每个信道包含相同的模式信号。 一个通道信号是“干净的”，另一个包括加性噪声。 使用来自这些信道信号的特征向量，确定噪声校正和缩放向量的集合。 当稍后接收到噪声模式信号的特征向量时，将其乘以该特征向量的最佳缩放向量，并将最佳校正向量加到乘积以产生降噪特征向量。 在一个实施例中，通过为噪声特征向量选择最佳混合分量来识别最佳缩放和校正矢量。 基于与每个混合物组分相关联的噪声通道特征向量的分布来选择最佳混合物组分。

公开(公告)号：US08433576B2

公开(公告)日：2013-04-30

申请号：US11655702

申请日：2007-01-19

申请人： Xiaolong Li ,  Yun-Cheng Ju ,  Li Deng ,  Alejandro Acero

发明人： Xiaolong Li ,  Yun-Cheng Ju ,  Li Deng ,  Alejandro Acero

IPC分类号： G10L15/22

CPC分类号： G06F17/271 ,  G09B17/003 ,  G10L15/197 ,  G10L2015/221

摘要： A novel system for automatic reading tutoring provides effective error detection and reduced false alarms combined with low processing time burdens and response times short enough to maintain a natural, engaging flow of interaction. According to one illustrative embodiment, an automatic reading tutoring method includes displaying a text output and receiving an acoustic input. The acoustic input is modeled with a domain-specific target language model specific to the text output, and with a general-domain garbage language model, both of which may be efficiently constructed as context-free grammars. The domain-specific target language model may be built dynamically or “on-the-fly” based on the currently displayed text (e.g. the story to be read by the user), while the general-domain garbage language model is shared among all different text outputs. User-perceptible tutoring feedback is provided based on the target language model and the garbage language model.

摘要翻译： 用于自动阅读辅导的新颖系统提供了有效的错误检测和减少的假警报以及较短的处理时间负担和响应时间足够短以保持自然的，互动的互动流。 根据一个说明性实施例，自动阅读辅导方法包括显示文本输出并接收声输入。 声输入是用专门针对文本输出的领域特定的目标语言模型建立的，并且具有通用域垃圾语言模型，这两种语言模型都可以被有效地构建为无上下文的语法。 可以基于当前显示的文本（例如，用户要阅读的故事）动态地或“即时”地构建特定领域的目标语言模型，而一般域垃圾语言模型在所有不同的方式之间共享 文本输出。 基于目标语言模型和垃圾语言模型提供了用户可感知的辅导反馈。

公开(公告)号：US08407041B2

公开(公告)日：2013-03-26

申请号：US12957394

申请日：2010-12-01

申请人： Li Deng ,  Yaodong Zhang ,  Alejandro Acero ,  Xiaodong He

发明人： Li Deng ,  Yaodong Zhang ,  Alejandro Acero ,  Xiaodong He

IPC分类号： G06F17/28

CPC分类号： G06F17/2818 ,  G10L15/14 ,  G10L15/183

摘要： Architecture that provides the integration of automatic speech recognition (ASR) and machine translation (MT) components of a full speech translation system. The architecture is an integrative and discriminative approach that employs an end-to-end objective function (the conditional probability of the translated sentence (target) given the source language's acoustic signal, as well as the associated BLEU score in the translation, as a goal in the integrated system. This goal defines the theoretically correct variables to determine the speech translation system output using a Bayesian decision rule. These theoretically correct variables are modified in practical use due to known imperfections of the various models used in building the full speech translation system. The disclosed approach also employs automatic training of these variables using minimum classification error (MCE) criterion. The measurable BLEU scores are used to facilitate the implementation of the MCE training procedure in a step that defines the class-specific discriminant function.

摘要翻译： 提供完整语音翻译系统的自动语音识别（ASR）和机器翻译（MT）组件的集成的架构。 该架构是一种综合和歧视性的方法，采用端到端目标函数（给定源语言的声信号的翻译句子（目标）的条件概率）以及翻译中相关联的BLEU得分作为目标 这个目标定义了理论上正确的变量来确定使用贝叶斯判决规则的语音翻译系统输出，这些理论上正确的变量在实际应用中被修改，这是由于建立全语音翻译系统中使用的各种模型的已知缺陷 所公开的方法还采用最小分类误差（MCE）标准对这些变量进行自动训练，可测量的BLEU分数用于在定义特定类别判别函数的步骤中促进MCE训练过程的实现。

公开(公告)号：US20120143591A1

公开(公告)日：2012-06-07

申请号：US12957394

申请日：2010-12-01

申请人： Li Deng ,  Yaodong Zhang ,  Alejandro Acero ,  Xiaodong He

发明人： Li Deng ,  Yaodong Zhang ,  Alejandro Acero ,  Xiaodong He

IPC分类号： G06F17/28

CPC分类号： G06F17/2818 ,  G10L15/14 ,  G10L15/183

摘要： Architecture that provides the integration of automatic speech recognition (ASR) and machine translation (MT) components of a full speech translation system. The architecture is an integrative and discriminative approach that employs an end-to-end objective function (the conditional probability of the translated sentence (target) given the source language's acoustic signal, as well as the associated BLEU score in the translation, as a goal in the integrated system. This goal defines the theoretically correct variables to determine the speech translation system output using a Bayesian decision rule. These theoretically correct variables are modified in practical use due to known imperfections of the various models used in building the full speech translation system. The disclosed approach also employs automatic training of these variables using minimum classification error (MCE) criterion. The measurable BLEU scores are used to facilitate the implementation of the MCE training procedure in a step that defines the class-specific discriminant function.

摘要翻译： 提供完整语音翻译系统的自动语音识别（ASR）和机器翻译（MT）组件的集成的架构。 该架构是一种综合和歧视性的方法，采用端到端目标函数（给定源语言的声信号的翻译句子（目标）的条件概率）以及翻译中相关联的BLEU得分作为目标 这个目标定义了理论上正确的变量来确定使用贝叶斯判决规则的语音翻译系统输出，这些理论上正确的变量在实际应用中被修改，这是由于建立全语音翻译系统中使用的各种模型的已知缺陷 所公开的方法还采用最小分类误差（MCE）标准对这些变量进行自动训练，可测量的BLEU分数用于在定义特定类别判别函数的步骤中促进MCE训练过程的实现。

公开(公告)号：US07424423B2

公开(公告)日：2008-09-09

申请号：US10404411

申请日：2003-04-01

申请人： Issam Bazzi ,  Li Deng ,  Alejandro Acero

发明人： Issam Bazzi ,  Li Deng ,  Alejandro Acero

IPC分类号： G10L19/04

CPC分类号： G10L15/02 ,  G10L25/15

摘要： A method of tracking formants defines a formant search space comprising sets of formants to be searched. Formants are identified for a first frame in the speech utterance by searching the entirety of the formant search space using the codebook, and for the remaining frames by searching the same space using both the codebook and the continuity constraint across adjacent frames. Under one embodiment, the formants are identified by mapping sets of formants into feature vectors and applying the feature vectors to a model. Formants are also identified by applying dynamic programming to search for the best sequence that optimally satisfies the continuity constraint required by the model.

摘要翻译： 跟踪共享器的方法定义了包括要搜索的共振峰集合的共振峰搜索空间。 通过使用码本搜索整体的共振峰搜索空间，并且通过使用码本和相邻帧之间的连续性约束搜索相同的空间，为语音语音中的第一帧识别共振峰。 在一个实施例中，通过将共振峰集合映射到特征向量中并将特征向量应用于模型来识别共振峰。 还通过应用动态规划来搜索最优序列，以最佳地满足模型所需的连续性约束，来确定共振峰。

公开(公告)号：US07363221B2

公开(公告)日：2008-04-22

申请号：US10643370

申请日：2003-08-19

申请人： James G. Droppo ,  Li Deng ,  Alejandro Acero

发明人： James G. Droppo ,  Li Deng ,  Alejandro Acero

IPC分类号： G10L21/02

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。