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21. 发明申请

US20100188412A1 CONTENT BASED CACHE FOR GRAPHICS RESOURCE MANAGEMENT 审中-公开
标题翻译：基于内容的图形资源管理缓存
公开(公告)号：US20100188412A1
公开(公告)日：2010-07-29
申请号：US12361216
申请日：2009-01-28
申请人： Chen Li , Jinyu Li , Xin Tong , Barry C. Bond , Gang Chen
发明人： Chen Li , Jinyu Li , Xin Tong , Barry C. Bond , Gang Chen
IPC分类号： G09G5/36
CPC分类号： G06T1/60 , G06F12/0875 , G09G2360/121
摘要： Providing content based cache for graphic resource management is disclosed herein. In some aspects, a portion of a shadow copy of graphics resources is updated from an original copy of the graphics resources when a requested resource is not current. The shadow copy may be dedicated to a graphics processing unit (GPU) while the original copy may be maintained by a central processing unit (CPU). In further aspects, the requested graphics resource in the shadow copy may be compared to a corresponding graphics resource in the original copy when the GPU requests the graphics resource. The comparison may be performed by comparing hashes of each graphics resource and/or by comparing at least a portion of the graphics resources.
摘要翻译：本文公开了提供用于图形资源管理的基于内容的缓存。在一些方面，当所请求的资源不是当前时，图形资源的卷影副本的一部分从图形资源的原始副本被更新。影子副本可以专用于图形处理单元（GPU），而原始副本可以由中央处理单元（CPU）维护。在另外的方面，当GPU请求图形资源时，可将影子副本中所请求的图形资源与原始副本中的对应图形资源进行比较。可以通过比较每个图形资源的哈希和/或通过比较图形资源的至少一部分来执行比较。

22. 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。

23. 发明授权

US09070360B2 Confidence calibration in automatic speech recognition systems 有权
标题翻译：自动语音识别系统中的置信度校准
公开(公告)号：US09070360B2
公开(公告)日：2015-06-30
申请号：US12634744
申请日：2009-12-10
申请人： Dong Yu , Li Deng , Jinyu Li
发明人： Dong Yu , Li Deng , Jinyu Li
IPC分类号： G10L15/06 , G10L15/18 , G10L15/183 , G10L15/01
CPC分类号： G10L15/01
摘要： Described is a calibration model for use in a speech recognition system. The calibration model adjusts the confidence scores output by a speech recognition engine to thereby provide an improved calibrated confidence score for use by an application. The calibration model is one that has been trained for a specific usage scenario, e.g., for that application, based upon a calibration training set obtained from a previous similar/corresponding usage scenario or scenarios. Different calibration models may be used with different usage scenarios, e.g., during different conditions. The calibration model may comprise a maximum entropy classifier with distribution constraints, trained with continuous raw confidence scores and multi-valued word tokens, and/or other distributions and extracted features.
摘要翻译：描述了一种用于语音识别系统的校准模型。校准模型调整由语音识别引擎输出的置信度得分，从而为应用程序提供改进的校准置信度得分。基于从先前的相似/相应的使用情景或情景获得的校准训练集，校准模型是针对特定使用场景（例如针对该应用）进行训练的模型。不同的校准模型可以用于不同的使用场合，例如在不同的条件下。校准模型可以包括具有分布约束的最大熵分类器，训练有连续原始置信分数和多值词令牌，和/或其他分布和提取的特征。

24. 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。

25. 发明申请

US20100318355A1 MODEL TRAINING FOR AUTOMATIC SPEECH RECOGNITION FROM IMPERFECT TRANSCRIPTION DATA 有权
标题翻译：用于自动语音识别的模型培训从不正确的转录数据
公开(公告)号：US20100318355A1
公开(公告)日：2010-12-16
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： G10L15/06
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。

26. 发明申请

US20100188404A1 SINGLE-PASS BOUNDING BOX CALCULATION 有权
标题翻译：单通道边框计算
公开(公告)号：US20100188404A1
公开(公告)日：2010-07-29
申请号：US12361676
申请日：2009-01-29
申请人： Xin Tong , Chen Li , Jinyu Li
发明人： Xin Tong , Chen Li , Jinyu Li
IPC分类号： G06T15/60
CPC分类号： G06T15/005 , G06T11/40 , G06T2210/12
摘要： Embodiments for single-pass bounding box calculation are disclosed. In accordance with one embodiment, the single-pass bounding box calculation includes rendering a first target to a 2-dimensional screen space, whereby the first target includes at least six pixels. The calculation further includes producing transformed vertices in a set of geometry primitives based on an application-specified transformation. The calculation also includes generating six new points for each transformed vertex in the set of geometry primitives. The calculation additionally includes producing an initial third coordinate value for each pixel by rendering the at least six new points generate for each pixel to each corresponding pixel. The calculation further includes producing a post-rasterization value for each pixel by rasterizing the at least six new points rendered to each pixel with each corresponding pixel. Finally, the calculation includes computing bounding box information for the set of geometry primitives based on the produced third coordinate values.
摘要翻译：公开了单程界限框计算的实施例。根据一个实施例，单程界限框计算包括将第一目标渲染到二维屏幕空间，由此第一目标包括至少六个像素。该计算还包括基于应用指定的变换在一组几何图元中产生经变换的顶点。该计算还包括为几何图元集合中的每个变换的顶点生成六个新点。该计算另外包括通过将针对每个像素生成的至少六个新点渲染到每个对应像素来产生每个像素的初始第三坐标值。该计算还包括通过用每个对应的像素光栅化渲染到每个像素的至少六个新点来为每个像素产生光栅后值。最后，计算包括基于产生的第三坐标值来计算几何图元集合的边界框信息。

27. 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。

28. 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。

29. 发明授权

US08217962B2 Single-pass bounding box calculation 有权
标题翻译：单程边界计算
公开(公告)号：US08217962B2
公开(公告)日：2012-07-10
申请号：US12361676
申请日：2009-01-29
申请人： Xin Tong , Chen Li , Jinyu Li
发明人： Xin Tong , Chen Li , Jinyu Li
IPC分类号： G09G5/00 , G06T15/00 , G06T15/40
CPC分类号： G06T15/005 , G06T11/40 , G06T2210/12
摘要： Embodiments for single-pass bounding box calculation are disclosed. In accordance with one embodiment, the single-pass bounding box calculation includes rendering a first target to a 2-dimensional screen space, whereby the first target includes at least six pixels. The calculation further includes producing transformed vertices in a set of geometry primitives based on an application-specified transformation. The calculation also includes generating six new points for each transformed vertex in the set of geometry primitives. The calculation additionally includes producing an initial third coordinate value for each pixel by rendering the at least six new points generate for each pixel to each corresponding pixel. The calculation further includes producing a post-rasterization value for each pixel by rasterizing the at least six new points rendered to each pixel with each corresponding pixel. Finally, the calculation includes computing bounding box information for the set of geometry primitives based on the produced third coordinate values.
摘要翻译：公开了单程界限框计算的实施例。根据一个实施例，单程界限框计算包括将第一目标渲染到二维屏幕空间，由此第一目标包括至少六个像素。该计算还包括基于应用指定的变换在一组几何图元中产生经变换的顶点。该计算还包括为几何图元集合中的每个变换的顶点生成六个新点。该计算另外包括通过将针对每个像素生成的至少六个新点渲染到每个对应像素来产生每个像素的初始第三坐标值。该计算还包括通过用每个对应的像素光栅化渲染到每个像素的至少六个新点来为每个像素产生光栅后值。最后，计算包括基于产生的第三坐标值来计算几何图元集合的边界框信息。

30. 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。

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