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1. 发明授权

US6151575A Rapid adaptation of speech models 失效
标题翻译：语音模型的快速适应
公开(公告)号：US6151575A
公开(公告)日：2000-11-21
申请号：US958957
申请日：1997-10-28
申请人： Michael Jack Newman , Laurence S. Gillick , Venkatesh Nagesha
发明人： Michael Jack Newman , Laurence S. Gillick , Venkatesh Nagesha
IPC分类号： G10L15/06 , G10L13/00
CPC分类号： G10L15/07
摘要： A source-adapted model for use in speech recognition is generated by defining a linear relationship between a first element of an initial model and a first element of the source-adapted model. Thereafter, speech data that corresponds to the first element of the initial model is assembled from a set of speech data for a particular source associated with the source-adapted model. A linear transform that maps between the assembled speech data and the first element of the initial model is then determined. Finally, a first element of the source-adapted model is produced from the first element of the initial model using the linear transform.
摘要翻译：通过定义初始模型的第一元素和源适应模型的第一元素之间的线性关系来生成用于语音识别的源适应模型。此后，对于与源适应模型相关联的特定源的一组语音数据组装对应于初始模型的第一元素的语音数据。然后确定在汇编的语音数据和初始模型的第一个元素之间映射的线性变换。最后，源适应模型的第一个元素是使用线性变换从初始模型的第一个元素生成的。

2. 发明授权

US09037463B2 Efficient exploitation of model complementariness by low confidence re-scoring in automatic speech recognition 有权
标题翻译：通过自动语音识别中的低置信度重新评估模型互补性的高效利用
公开(公告)号：US09037463B2
公开(公告)日：2015-05-19
申请号：US13518409
申请日：2010-05-27
申请人： Daniel Willett , Venkatesh Nagesha
发明人： Daniel Willett , Venkatesh Nagesha
IPC分类号： G10L15/04 , G10L15/14 , G10L15/183 , G10L15/32
CPC分类号： G10L15/142 , G10L15/183 , G10L15/32
摘要： A method for speech recognition is described that uses an initial recognizer to perform an initial speech recognition pass on an input speech utterance to determine an initial recognition result corresponding to the input speech utterance, and a reliability measure reflecting a per word reliability of the initial recognition result. For portions of the initial recognition result where the reliability of the result is low, a re-evaluation recognizer is used to perform a re-evaluation recognition pass on the corresponding portions of the input speech utterance to determine a re-evaluation recognition result corresponding to the re-evaluated portions of the input speech utterance. The initial recognizer and the re-evaluation recognizer are complementary so as to make different recognition errors. A final recognition result is determined based on the re-evaluation recognition result if any, and otherwise based on the initial recognition result.
摘要翻译：描述了一种用于语音识别的方法，其使用初始识别器对输入语音话语执行初始语音识别，以确定对应于输入语音话语的初始识别结果，以及反映初始识别的每字可靠性的可靠性度量结果。对于结果可靠性低的初始识别结果的部分，使用重新评估识别器对输入语音话语的相应部分执行重新评估识别，以确定对应于重新评估输入语音话语的部分。初始识别器和重新评估识别器是互补的，以便产生不同的识别错误。根据重新评估识别结果（如果有的话），否则根据初始识别结果确定最终识别结果。

3. 发明授权

US08768695B2 Channel normalization using recognition feedback 有权
标题翻译：频道归一化使用识别反馈
公开(公告)号：US08768695B2
公开(公告)日：2014-07-01
申请号：US13495509
申请日：2012-06-13
申请人： Yun Tang , Venkatesh Nagesha
发明人： Yun Tang , Venkatesh Nagesha
IPC分类号： G10L19/14 , G10L15/00
CPC分类号： G10L15/20 , G10L15/065 , G10L21/0208
摘要： A computer-implemented arrangement is described for performing cepstral mean normalization (CMN) in automatic speech recognition. A current CMN function is stored in a computer memory as a previous CMN function. The current CMN function is updated based on a current audio input to produce an updated CMN function. The updated CMN function is used to process the current audio input to produce a processed audio input. Automatic speech recognition of the processed audio input is performed to determine representative text. If the audio input is not recognized as representative text, the updated CMN function is replaced with the previous CMN function.
摘要翻译：描述了用于在自动语音识别中执行倒谱平均归一化（CMN）的计算机实现的布置。当前CMN功能作为先前的CMN功能存储在计算机存储器中。基于当前音频输入更新当前CMN功能以产生更新的CMN功能。更新的CMN功能用于处理当前音频输入以产生经处理的音频输入。执行经处理的音频输入的自动语音识别以确定代表性的文本。如果音频输入不被识别为代表性文本，则更新的CMN功能被替换为先前的CMN功能。

4. 发明申请

US20130317820A1 Automatic Methods to Predict Error Rates and Detect Performance Degradation 有权
标题翻译：自动方法预测错误率和检测性能下降
公开(公告)号：US20130317820A1
公开(公告)日：2013-11-28
申请号：US13479945
申请日：2012-05-24
申请人： Xiaoqiang Xiao , Venkatesh Nagesha
发明人： Xiaoqiang Xiao , Venkatesh Nagesha
IPC分类号： G10L15/00
CPC分类号： G10L15/01 , G10L15/065
摘要： An automatic speech recognition dictation application is described that includes a dictation module for performing automatic speech recognition in a dictation session with a speaker user to determine representative text corresponding to input speech from the speaker user. A post-processing module develops a session level metric correlated to verbatim recognition error rate of the dictation session, and determines if recognition performance degraded during the dictation session based on a comparison of the session metric to a baseline metric.
摘要翻译：描述了一种自动语音识别听写应用程序，其包括用于在与扬声器用户的听写会话中执行自动语音识别的听写模块，以从扬声器用户确定对应于输入语音的代表性文本。后处理模块开发与听写会话的逐字识别错误率相关的会话级别度量，并且基于会话度量与基准度量的比较来确定在听写会话期间识别性能是否下降。

5. 发明授权

US09269349B2 Automatic methods to predict error rates and detect performance degradation 有权
标题翻译：自动预测错误率和检测性能下降的方法
公开(公告)号：US09269349B2
公开(公告)日：2016-02-23
申请号：US13479945
申请日：2012-05-24
申请人： Xiaoqiang Xiao , Venkatesh Nagesha
发明人： Xiaoqiang Xiao , Venkatesh Nagesha
IPC分类号： G10L15/06 , G10L15/01 , G10L15/065
CPC分类号： G10L15/01 , G10L15/065
摘要： An automatic speech recognition dictation application is described that includes a dictation module for performing automatic speech recognition in a dictation session with a speaker user to determine representative text corresponding to input speech from the speaker user. A post-processing module develops a session level metric correlated to verbatim recognition error rate of the dictation session, and determines if recognition performance degraded during the dictation session based on a comparison of the session metric to a baseline metric.
摘要翻译：描述了一种自动语音识别听写应用程序，其包括用于在与扬声器用户的听写会话中执行自动语音识别的听写模块，以从扬声器用户确定对应于输入语音的代表性文本。后处理模块开发与听写会话的逐字识别错误率相关的会话级别度量，并且基于会话度量与基准度量的比较来确定在听写会话期间识别性能是否下降。

6. 发明申请

US20130339014A1 Channel Normalization Using Recognition Feedback 有权
标题翻译：信道归一化使用识别反馈
公开(公告)号：US20130339014A1
公开(公告)日：2013-12-19
申请号：US13495509
申请日：2012-06-13
申请人： Yun Tang , Venkatesh Nagesha
发明人： Yun Tang , Venkatesh Nagesha
IPC分类号： G10L15/26
CPC分类号： G10L15/20 , G10L15/065 , G10L21/0208
摘要： A computer-implemented arrangement is described for performing cepstral mean normalization (CMN) in automatic speech recognition. A current CMN function is stored in a computer memory as a previous CMN function. The current CMN function is updated based on a current audio input to produce an updated CMN function. The updated CMN function is used to process the current audio input to produce a processed audio input. Automatic speech recognition of the processed audio input is performed to determine representative text. If the audio input is not recognized as representative text, the updated CMN function is replaced with the previous CMN function.
摘要翻译：描述了用于在自动语音识别中执行倒谱平均归一化（CMN）的计算机实现的布置。当前CMN功能作为先前的CMN功能存储在计算机存储器中。基于当前音频输入更新当前CMN功能以产生更新的CMN功能。更新的CMN功能用于处理当前音频输入以产生经处理的音频输入。执行经处理的音频输入的自动语音识别以确定代表性的文本。如果音频输入不被识别为代表性文本，则更新的CMN功能被替换为先前的CMN功能。

7. 发明申请

US20120259627A1 Efficient Exploitation of Model Complementariness by Low Confidence Re-Scoring in Automatic Speech Recognition 有权
标题翻译：自动语音识别中低信度重新评估模型互补性的有效开发
公开(公告)号：US20120259627A1
公开(公告)日：2012-10-11
申请号：US13518409
申请日：2010-05-27
申请人： Daniel Willett , Venkatesh Nagesha
发明人： Daniel Willett , Venkatesh Nagesha
IPC分类号： G10L15/00
CPC分类号： G10L15/142 , G10L15/183 , G10L15/32
摘要： A method for speech recognition is described that uses an initial recognizer to perform an initial speech recognition pass on an input speech utterance to determine an initial recognition result corresponding to the input speech utterance, and a reliability measure reflecting a per word reliability of the initial recognition result. For portions of the initial recognition result where the reliability of the result is low, a re-evaluation recognizer is used to perform a re-evaluation recognition pass on the corresponding portions of the input speech utterance to determine a re-evaluation recognition result corresponding to the re-evaluated portions of the input speech utterance. The initial recognizer and the re-evaluation recognizer are complementary so as to make different recognition errors. A final recognition result is determined based on the re-evaluation recognition result if any, and otherwise based on the initial recognition result.
摘要翻译：描述了一种用于语音识别的方法，其使用初始识别器对输入语音话语执行初始语音识别，以确定对应于输入语音话语的初始识别结果，以及反映初始识别的每字可靠性的可靠性度量结果。对于结果可靠性低的初始识别结果的部分，使用重新评估识别器对输入语音话语的相应部分执行重新评估识别，以确定对应于重新评估输入语音话语的部分。初始识别器和重新评估识别器是互补的，以便产生不同的识别错误。根据重新评估识别结果（如果有的话），否则根据初始识别结果确定最终识别结果。

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