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1. 发明授权

US08731916B2 Online distorted speech estimation within an unscented transformation framework 有权
标题翻译：一个无限转换框架内的在线扭曲语音估计
公开(公告)号：US08731916B2
公开(公告)日：2014-05-20
申请号：US12948935
申请日：2010-11-18
申请人： Deng Li , Jinyu Li , Dong Yu , Yifan Gong
发明人： Deng Li , Jinyu Li , Dong Yu , Yifan Gong
IPC分类号： G10L21/02
CPC分类号： G10L19/005 , G10L15/20
摘要： Noise and channel distortion parameters in the vectorized logarithmic or the cepstral domain for an utterance may be estimated, and subsequently the distorted speech parameters in the same domain may be updated using an unscented transformation framework during online automatic speech recognition. An utterance, including speech generated from a transmission source for delivery to a receiver, may be received by a computing device. The computing device may execute instructions for applying the unscented transformation framework to speech feature vectors, representative of the speech, in order to estimate, in a sequential or online manner, static noise and channel distortion parameters and dynamic noise distortion parameters in the unscented transformation framework. The static and dynamic parameters for the distorted speech in the utterance may then be updated from clean speech parameters and the noise and channel distortion parameters using non-linear mapping.
摘要翻译：可以估计用于话语的向量化对数或倒频域中的噪声和信道失真参数，并且随后可以在在线自动语音识别期间使用无密码变换框架来更新相同域中的失真语音参数。包括从发送源产生的用于传送到接收机的语音的话语可以被计算设备接收。计算设备可以执行用于将无声变换框架应用于代表语音的语音特征向量的指令，以便以顺序或在线方式估计无密度变换框架中的静态噪声和信道失真参数以及动态噪声失真参数。然后可以使用非线性映射从干净的语音参数和噪声和信道失真参数中更新话音中失真语音的静态和动态参数。

2. 发明申请

US20120130710A1 ONLINE DISTORTED SPEECH ESTIMATION WITHIN AN UNSCENTED TRANSFORMATION FRAMEWORK 有权
标题翻译：在一个未经规定的转换框架内的在线失真的语音估计
公开(公告)号：US20120130710A1
公开(公告)日：2012-05-24
申请号：US12948935
申请日：2010-11-18
申请人： Deng Li , Jinyu Li , Dong Yu , Yifan Gong
发明人： Deng Li , Jinyu Li , Dong Yu , Yifan Gong
IPC分类号： G10L15/00
CPC分类号： G10L19/005 , G10L15/20
摘要： Noise and channel distortion parameters in the vectorized logarithmic or the cepstral domain for an utterance may be estimated, and subsequently the distorted speech parameters in the same domain may be updated using an unscented transformation framework during online automatic speech recognition. An utterance, including speech generated from a transmission source for delivery to a receiver, may be received by a computing device. The computing device may execute instructions for applying the unscented transformation framework to speech feature vectors, representative of the speech, in order to estimate, in a sequential or online manner, static noise and channel distortion parameters and dynamic noise distortion parameters in the unscented transformation framework. The static and dynamic parameters for the distorted speech in the utterance may then be updated from clean speech parameters and the noise and channel distortion parameters using non-linear mapping.
摘要翻译：可以估计用于话语的向量化对数或倒频域中的噪声和信道失真参数，并且随后可以在在线自动语音识别期间使用无密码变换框架来更新相同域中的失真语音参数。包括从发送源产生的用于传送到接收机的语音的话语可以被计算设备接收。计算设备可以执行用于将无声变换框架应用于代表语音的语音特征向量的指令，以便以顺序或在线方式估计无密度变换框架中的静态噪声和信道失真参数以及动态噪声失真参数。然后可以使用非线性映射从干净的语音参数和噪声和信道失真参数中更新话音中失真语音的静态和动态参数。

3. 发明申请

US20090144059A1 HIGH PERFORMANCE HMM ADAPTATION WITH JOINT COMPENSATION OF ADDITIVE AND CONVOLUTIVE DISTORTIONS 有权
标题翻译：高性能HMM适应与补充和转换失败的联合补偿
公开(公告)号：US20090144059A1
公开(公告)日：2009-06-04
申请号：US11949044
申请日：2007-12-03
申请人： Dong Yu , Li Deng , Alejandro Acero , Yifan Gong , Jinyu Li
发明人： Dong Yu , Li Deng , Alejandro Acero , Yifan Gong , Jinyu Li
IPC分类号： G10L15/14
CPC分类号： G10L15/20 , G10L15/142
摘要： A method of compensating for additive and convolutive distortions applied to a signal indicative of an utterance is discussed. The method includes receiving a signal and initializing noise mean and channel mean vectors. Gaussian dependent matrix and Hidden Markov Model (HMM) parameters are calculated or updated to account for additive noise from the noise mean vector or convolutive distortion from the channel mean vector. The HMM parameters are adapted by decoding the utterance using the previously calculated HMM parameters and adjusting the Gaussian dependent matrix and the HMM parameters based upon data received during the decoding. The adapted HMM parameters are applied to decode the input utterance and provide a transcription of the utterance.
摘要翻译：讨论了补偿施加到表示话语的信号的加法和卷积失真的方法。该方法包括接收信号并初始化噪声平均和信道均值向量。计算或更新高斯依赖矩阵和隐马尔可夫模型（HMM）参数以考虑来自信道平均向量的噪声平均向量或卷积失真的加性噪声。 HMM参数通过使用先前计算出的HMM参数解码话音并根据解码期间接收到的数据调整高斯相关矩阵和HMM参数进行调整。适应的HMM参数被应用于解码输入的话语并提供话语的转录。

4. 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。

5. 发明申请

US20100076758A1 PHASE SENSITIVE MODEL ADAPTATION FOR NOISY SPEECH RECOGNITION 有权
标题翻译：语音识别的相敏感模型适应
公开(公告)号：US20100076758A1
公开(公告)日：2010-03-25
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： G10L15/20 , 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。

6. 发明授权

US08180637B2 High performance HMM adaptation with joint compensation of additive and convolutive distortions 有权
标题翻译：高性能HMM适应与加法和卷积扭曲的联合补偿
公开(公告)号：US08180637B2
公开(公告)日：2012-05-15
申请号：US11949044
申请日：2007-12-03
申请人： Dong Yu , Li Deng , Alejandro Acero , Yifan Gong , Jinyu Li
发明人： Dong Yu , Li Deng , Alejandro Acero , Yifan Gong , Jinyu Li
IPC分类号： G10L15/00 , G10L15/20 , G10L17/00
CPC分类号： G10L15/20 , G10L15/142
摘要： A method of compensating for additive and convolutive distortions applied to a signal indicative of an utterance is discussed. The method includes receiving a signal and initializing noise mean and channel mean vectors. Gaussian dependent matrix and Hidden Markov Model (HMM) parameters are calculated or updated to account for additive noise from the noise mean vector or convolutive distortion from the channel mean vector. The HMM parameters are adapted by decoding the utterance using the previously calculated HMM parameters and adjusting the Gaussian dependent matrix and the HMM parameters based upon data received during the decoding. The adapted HMM parameters are applied to decode the input utterance and provide a transcription of the utterance.
摘要翻译：讨论了补偿施加到表示话语的信号的加法和卷积失真的方法。该方法包括接收信号并初始化噪声平均和信道均值向量。计算或更新高斯依赖矩阵和隐马尔可夫模型（HMM）参数以考虑来自信道平均向量的噪声平均向量或卷积失真的加性噪声。 HMM参数通过使用先前计算出的HMM参数解码话音并根据解码期间接收到的数据调整高斯相关矩阵和HMM参数进行调整。适应的HMM参数被应用于解码输入的话语并提供话语的转录。

7. 发明申请

US20100076757A1 ADAPTING A COMPRESSED MODEL FOR USE IN SPEECH RECOGNITION 有权
标题翻译：适应用于语音识别的压缩模型
公开(公告)号：US20100076757A1
公开(公告)日：2010-03-25
申请号：US12235748
申请日：2008-09-23
申请人： Jinyu Li , Li Deng , Dong Yu , Jian Wu , Yifan Gong , Alejandro Acero
发明人： Jinyu Li , Li Deng , Dong Yu , Jian Wu , Yifan Gong , Alejandro Acero
IPC分类号： G10L15/20
CPC分类号： G10L15/20 , G10L15/065
摘要： A speech recognition system includes a receiver component that receives a distorted speech utterance. The speech recognition also includes an adaptor component that selectively adapts parameters of a compressed model used to recognize at least a portion of the distorted speech utterance, wherein the adaptor component selectively adapts the parameters of the compressed model based at least in part upon the received distorted speech utterance.
摘要翻译：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适配器组件，所述适配器组件选择性地适配用于识别所述失真语音话语的至少一部分的压缩模型的参数，其中所述适配器组件至少部分地基于接收失真的语音话语选择性地调整所述压缩模型的参数讲话话语。

8. 发明授权

US08239195B2 Adapting a compressed model for use in speech recognition 有权
标题翻译：适应用于语音识别的压缩模型
公开(公告)号：US08239195B2
公开(公告)日：2012-08-07
申请号：US12235748
申请日：2008-09-23
申请人： Jinyu Li , Li Deng , Dong Yu , Jian Wu , Yifan Gong , Alejandro Acero
发明人： Jinyu Li , Li Deng , Dong Yu , Jian Wu , Yifan Gong , Alejandro Acero
IPC分类号： G10L15/20
CPC分类号： G10L15/20 , G10L15/065
摘要： A speech recognition system includes a receiver component that receives a distorted speech utterance. The speech recognition also includes an adaptor component that selectively adapts parameters of a compressed model used to recognize at least a portion of the distorted speech utterance, wherein the adaptor component selectively adapts the parameters of the compressed model based at least in part upon the received distorted speech utterance.
摘要翻译：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适配器组件，所述适配器组件选择性地适配用于识别所述失真语音话语的至少一部分的压缩模型的参数，其中所述适配器组件至少部分地基于接收失真的语音话语选择性地调整所述压缩模型的参数讲话话语。

9. 发明授权

US09280969B2 Model training for automatic speech recognition from imperfect transcription data 有权
标题翻译：从不完美的转录数据自动语音识别的模型训练
公开(公告)号：US09280969B2
公开(公告)日：2016-03-08
申请号：US12482142
申请日：2009-06-10
申请人： Jinyu Li , Yifan Gong , Chaojun Liu , Kaisheng Yao
发明人： Jinyu Li , Yifan Gong , Chaojun Liu , Kaisheng Yao
IPC分类号： G10L15/00 , G10L15/06 , G10L15/065
CPC分类号： G10L15/063 , G10L15/065
摘要： Techniques and systems for training an acoustic model are described. In an embodiment, a technique for training an acoustic model includes dividing a corpus of training data that includes transcription errors into N parts, and on each part, decoding an utterance with an incremental acoustic model and an incremental language model to produce a decoded transcription. The technique may further include inserting silence between a pair of words into the decoded transcription and aligning an original transcription corresponding to the utterance with the decoded transcription according to time for each part. The technique may further include selecting a segment from the utterance having at least Q contiguous matching aligned words, and training the incremental acoustic model with the selected segment. The trained incremental acoustic model may then be used on a subsequent part of the training data. Other embodiments are described and claimed.
摘要翻译：描述了用于训练声学模型的技术和系统。在一个实施例中，用于训练声学模型的技术包括将包括转录错误的训练数据的语料库划分成N个部分，并且在每个部分上，用增量声学模型和增量语言模型解码语音以产生解码的转录。该技术可以进一步包括将一对单词之间的沉默插入解码的转录中，并根据每个部分的时间将与发音对应的原始转录与解码的转录对准。该技术可以进一步包括从具有至少Q个连续匹配对齐字的话语中选择一段，以及使用所选择的段来训练增量声学模型。然后可以在训练数据的后续部分上使用经过训练的增量声学模型。描述和要求保护其他实施例。

10. 发明申请

US20140067387A1 Utilizing Scalar Operations for Recognizing Utterances During Automatic Speech Recognition in Noisy Environments 审中-公开
标题翻译：在嘈杂环境中自动语音识别期间利用标量运算识别语音
公开(公告)号：US20140067387A1
公开(公告)日：2014-03-06
申请号：US13603796
申请日：2012-09-05
申请人： Jinyu Li , Michael Lewis Seltzer , Yifan Gong
发明人： Jinyu Li , Michael Lewis Seltzer , Yifan Gong
IPC分类号： G10L15/20
CPC分类号： G10L15/20 , G10L15/14 , G10L2015/226
摘要： Scalar operations for model adaptation or feature enhancement may be utilized for recognizing an utterance during automatic speech recognition in a noisy environment. An utterance including distorted speech generated from a transmission source for delivery to a receiver, may be received by a computer. The distorted speech may be caused by the noisy environment and channel distortion. Computations using scalar operations in the form of an algorithm may then be performed for recognizing the utterance. As a result of performing all of the computations with scalar operations, computational complexity is very small in comparison to matrix and vector operations. Vector Taylor Series with diagonal Jacobian approximation may also be utilized as a distortion-model-based noise robust algorithm with scalar operations.
摘要翻译：用于模型适应或特征增强的标量运算可以用于在噪声环境中的自动语音识别期间识别语音。可以由计算机接收包括从发送源产生的用于传送到接收器的失真语音的话语。失真的语音可能是由于嘈杂的环境和频道失真引起的。然后可以执行以算法形式使用标量运算的计算，以识别话语。作为使用标量运算执行所有计算的结果，与矩阵和向量运算相比，计算复杂度非常小。具有对角雅可比近似的矢量泰勒系列也可以用作具有标量运算的基于失真模型的噪声鲁棒算法。

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