公开(公告)号：US07599837B2

公开(公告)日：2009-10-06

申请号：US10941439

申请日：2004-09-15

申请人： Ye-Yi Wang ,  Yun-Cheng Ju ,  Leonard Alan Collins ,  Mark Cecys ,  Alejandro Acero

发明人： Ye-Yi Wang ,  Yun-Cheng Ju ,  Leonard Alan Collins ,  Mark Cecys ,  Alejandro Acero

IPC分类号： G10L15/00 ,  G10L15/18

CPC分类号： G10L15/193 ,  G10L15/183

摘要： A method and system to generate a grammar adapted for use by a speech recognizer includes receiving a representation of an alphanumeric expression. For instance, the representation can take the form of a regular expression or a mask. The grammar is generated based on the representation.

摘要翻译： 用于生成适于由语音识别器使用的语法的方法和系统包括接收字母数字表达式的表示。 例如，表示可以采用正则表达式或掩码的形式。 语法是基于表示生成的。

公开(公告)号：US20090226005A1

公开(公告)日：2009-09-10

申请号：US12464390

申请日：2009-05-12

申请人： Alejandro Acero ,  Ivan J. Tashev ,  Michael L. Seltzer

发明人： Alejandro Acero ,  Ivan J. Tashev ,  Michael L. Seltzer

IPC分类号： H04R3/00 ,  G10L21/02

CPC分类号： G10L21/0208 ,  G10L2021/02166

摘要： A noise reduction system and a method of noise reduction includes a microphone array comprising a first microphone, a second microphone, and a third microphone. Each microphone has a known position and a known directivity pattern. An instantaneous direction-of-arrival (IDOA) module determines a first phase difference quantity and a second phase difference quantity. The first phase difference quantity is based on phase differences between non-repetitive pairs of input signals received by the first microphone and the second microphone, while the second phase difference quantity is based on phase differences between non-repetitive pairs of input signals received by the first microphone and the third microphone. A spatial noise reduction module computes an estimate of a desired signal based on a priori spatial signal-to-noise ratio and an a posteriori spatial signal-to-noise ratio based on the first and second phase difference quantities.

摘要翻译： 降噪系统和降噪方法包括麦克风阵列，其包括第一麦克风，第二麦克风和第三麦克风。 每个麦克风具有已知的位置和已知的方向性图案。 瞬时到达方向（IDOA）模块确定第一相位差量和第二相位差量。 第一相位差量基于由第一麦克风和第二麦克风接收的非重复输入信号对之间的相位差，而第二相位差量基于由第一麦克风和第二麦克风接收的输入信号的非重复对之间的相位差， 第一麦克风和第三麦克风。 空间噪声降低模块基于先验空间信噪比和基于第一和第二相位差量的后验空间信噪比来计算期望信号的估计。

公开(公告)号：US07571097B2

公开(公告)日：2009-08-04

申请号：US10388260

申请日：2003-03-13

申请人： Alejandro Acero ,  Michael D. Plumpe

发明人： Alejandro Acero ,  Michael D. Plumpe

IPC分类号： G10L15/14 ,  G10L15/06

CPC分类号： G10L15/144 ,  G10L15/285

摘要： A method for compressing multiple dimensional gaussian distributions with diagonal covariance matrixes includes clustering a plurality of gaussian distributions in a multiplicity of clusters for each dimension. Each cluster can be represented by a centroid having a mean and a variance. A total decrease in likelihood of a training dataset is minimized for the representation of the plurality of gaussian distributions.

摘要翻译： 用对角协方差矩阵压缩多维高斯分布的方法包括针对每个维度在多个簇中聚类多个高斯分布。 每个簇可以由具有平均值和方差的质心来表示。 对于多个高斯分布的表示，训练数据集的可能性的总的降低被最小化。

公开(公告)号：US07548847B2

公开(公告)日：2009-06-16

申请号：US10142623

申请日：2002-05-10

申请人： Alejandro Acero ,  Ye-Yi Wang ,  Leon Wong

发明人： Alejandro Acero ,  Ye-Yi Wang ,  Leon Wong

IPC分类号： G06F17/28 ,  G10L15/00 ,  G10L15/18

CPC分类号： G06F17/2785 ,  G06F17/241

摘要： The present invention uses a natural language understanding system that is currently being trained to assist in annotating training data for training that natural language understanding system. Unannotated training data is provided to the system and the system proposes annotations to the training data. The user is offered an opportunity to confirm or correct the proposed annotations, and the system is trained with the corrected or verified annotations.

摘要翻译： 本发明使用一种自然语言理解系统，该系统正在被训练以协助注释训练数据以训练该自然语言理解系统。 提供未指定的训练数据到系统，并且系统提出对训练数据的注释。 提供给用户确认或更正所提出的注释的机会，并且使用经更正或验证的注释来对系统进行培训。

公开(公告)号：US07478038B2

公开(公告)日：2009-01-13

申请号：US10814906

申请日：2004-03-31

申请人： Ciprian Chelba ,  Milind Mahajan ,  Alejandro Acero ,  Yik-Cheung Tam

发明人： Ciprian Chelba ,  Milind Mahajan ,  Alejandro Acero ,  Yik-Cheung Tam

IPC分类号： G06F17/21

CPC分类号： G06F17/27 ,  G10L15/1815

摘要： A method and apparatus are provided for adapting a language model. The method and apparatus provide supervised class-based adaptation of the language model utilizing in-domain semantic information.

摘要翻译： 提供了一种适应语言模型的方法和装置。 该方法和装置使用域内语义信息来提供对语言模型进行监督的基于类的适应。

公开(公告)号：US07447630B2

公开(公告)日：2008-11-04

申请号：US10724008

申请日：2003-11-26

申请人： Zicheng Liu ,  Michael J. Sinclair ,  Alejandro Acero ,  Xuedong D. Huang ,  James G. Droppo ,  Li Deng ,  Zhengyou Zhang ,  Yanli Zheng

发明人： Zicheng Liu ,  Michael J. Sinclair ,  Alejandro Acero ,  Xuedong D. Huang ,  James G. 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。

公开(公告)号：US07409346B2

公开(公告)日：2008-08-05

申请号：US11069474

申请日：2005-03-01

申请人： Alejandro Acero ,  Dong Yu ,  Li Deng

发明人： Alejandro Acero ,  Dong Yu ,  Li Deng

IPC分类号： G10L15/10

CPC分类号： G10L15/02 ,  G10L25/15 ,  G10L25/24 ,  G10L2015/025

摘要： A structured generative model of a speech coarticulation and reduction is described with a novel two-stage implementation. At the first stage, the dynamics of formants or vocal tract resonance (VTR) are generated using prior information of resonance targets in the phone sequence. Bi-directional temporal filtering with finite impulse response (FIR) is applied to the segmental target sequence as the FIR filter's input. At the second stage the dynamics of speech cepstra are predicted analytically based on the FIR filtered VTR targets. The combined system of these two stages thus generates correlated and causally related VTR and cepstral dynamics where phonetic reduction is represented explicitly in the hidden resonance space and implicitly in the observed cepstral space. The combined system also gives the acoustic observation probability given a phone sequence. Using this probability, different phone sequences can be compared and ranked in terms of their respective probability values. This then permits the use of the model for phonetic recognition.

摘要翻译： 用新的两阶段实现来描述语音合成和简化的结构化生成模型。 在第一阶段，使用电话序列中共振目标的先前信息产生共振峰或声道共振（VTR）的动力学。 具有有限脉冲响应（FIR）的双向时间滤波作为FIR滤波器的输入应用于分段目标序列。 在第二阶段，基于FIR滤波的VTR目标，分析地预测语音cepstra的动力学。 这两个阶段的组合系统因此产生相关和因果相关的VTR和倒谱动力学，其中语音减少在隐藏共振空间中明确表示，并且隐含地在观察到的倒频谱空间中。 组合系统还给出了电话序列的声学观察概率。 使用这种概率，可以根据它们各自的概率值对不同的电话序列进行比较和排序。 这样就允许使用模型进行语音识别。

公开(公告)号：US20080177547A1

公开(公告)日：2008-07-24

申请号：US11655703

申请日：2007-01-19

申请人： Sibel Yaman ,  Li Deng ,  Dong Yu ,  Ye-Yi Wang ,  Alejandro Acero

发明人： Sibel Yaman ,  Li Deng ,  Dong Yu ,  Ye-Yi Wang ,  Alejandro Acero

IPC分类号： G10L15/18

CPC分类号： G10L15/1815

摘要： A novel system integrates speech recognition and semantic classification, so that acoustic scores in a speech recognizer that accepts spoken utterances may be taken into account when training both language models and semantic classification models. For example, a joint association score may be defined that is indicative of a correspondence of a semantic class and a word sequence for an acoustic signal. The joint association score may incorporate parameters such as weighting parameters for signal-to-class modeling of the acoustic signal, language model parameters and scores, and acoustic model parameters and scores. The parameters may be revised to raise the joint association score of a target word sequence with a target semantic class relative to the joint association score of a competitor word sequence with the target semantic class. The parameters may be designed so that the semantic classification errors in the training data are minimized.

摘要翻译： 一种新颖的系统集成了语音识别和语义分类，从而在训练语言模型和语义分类模型时，可以考虑接受讲话语音的语音识别器中的声学分数。 例如，可以定义联合关联分数，其表示声学信号的语义类别和单词序列的对应关系。 联合关联分数可以包括参数，例如声信号的信号到类建模的加权参数，语言模型参数和分数，以及声学模型参数和分数。 可以修改参数以相对于具有目标语义类的竞争者词序列的联合关联分数来提高具有目标语义类别的目标词序列的联合关联分数。 可以设计参数，使得训练数据中的语义分类误差最小化。

公开(公告)号：US07328147B2

公开(公告)日：2008-02-05

申请号：US10406524

申请日：2003-04-03

申请人： YeYi Wang ,  Alejandro Acero

发明人： YeYi Wang ,  Alejandro Acero

IPC分类号： G06F17/27

CPC分类号： G06F17/271 ,  G10L15/18

摘要： A rules-based grammar is generated. Segmentation ambiguities are identified in training data. Rewrite rules for the ambiguous segmentations are enumerated and probabilities are generated for each. Ambiguities are resolved based on the probabilities. In one embodiment, this is done by applying the expectation maximization (EM) algorithm.

摘要翻译： 生成基于规则的语法。 在训练数据中识别分割模糊。 枚举模糊分段的重写规则，并为每个生成概率。 根据概率解决歧义。 在一个实施例中，这通过应用期望最大化（EM）算法来完成。

公开(公告)号：US07254536B2

公开(公告)日：2007-08-07

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： G10L21/02

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。