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11. 发明授权

US07251603B2 Audio-only backoff in audio-visual speech recognition system 有权
标题翻译：音视频语音识别系统中的音频回退
公开(公告)号：US07251603B2
公开(公告)日：2007-07-31
申请号：US10601350
申请日：2003-06-23
申请人： Jonathan H. Connell , Norman Haas , Etienne Marcheret , Chalapathy Venkata Neti , Gerasimos Potamianos
发明人： Jonathan H. Connell , Norman Haas , Etienne Marcheret , Chalapathy Venkata Neti , Gerasimos Potamianos
IPC分类号： G10L21/00
CPC分类号： G10L15/25
摘要： Techniques for performing audio-visual speech recognition, with improved recognition performance, in a degraded visual environment. For example, in one aspect of the invention, a technique for use in accordance with an audio-visual speech recognition system for improving a recognition performance thereof includes the steps/operations of: (i) selecting between an acoustic-only data model and an acoustic-visual data model based on a condition associated with a visual environment; and (ii) decoding at least a portion of an input spoken utterance using the selected data model. Advantageously, during periods of degraded visual conditions, the audio-visual speech recognition system is able to decode (recognize) input speech data using audio-only data, thus avoiding recognition inaccuracies that may result from performing speech recognition based on acoustic-visual data models and degraded visual data.
摘要翻译：在劣化的视觉环境中执行视听语音识别技术，具有改进的识别性能。例如，在本发明的一个方面，根据用于改善其识别性能的视听语音识别系统使用的技术包括以下步骤/操作：（i）在仅声学数据模型和基于与视觉环境相关的条件的声学可视数据模型; 以及（ii）使用所选择的数据模型解码输入口头发音的至少一部分。有利的是，在恶化的视觉条件期间，视听语音识别系统能够使用仅音频数据解码（识别）输入语音数据，从而避免了基于声学可视数据模型执行语音识别可能导致的识别不准确并降低视觉数据。

12. 发明授权

US08386249B2 Compressing feature space transforms 有权
标题翻译：压缩特征空间转换
公开(公告)号：US08386249B2
公开(公告)日：2013-02-26
申请号：US12636033
申请日：2009-12-11
申请人： Petr Fousek , Vaibhava Goel , Etienne Marcheret , Peder Andreas Olsen
发明人： Petr Fousek , Vaibhava Goel , Etienne Marcheret , Peder Andreas Olsen
IPC分类号： G10L15/06
CPC分类号： G10L19/0212 , G10L19/032
摘要： Methods for compressing a transform associated with a feature space are presented. For example, a method for compressing a transform associated with a feature space includes obtaining the transform including a plurality of transform parameters, assigning each of a plurality of quantization levels for the plurality of transform parameters to one of a plurality of quantization values, and assigning each of the plurality of transform parameters to one of the plurality of quantization values to which one of the plurality of quantization levels is assigned. One or more of obtaining the transform, assigning of each of the plurality of quantization levels, and assigning of each of the transform parameters are implemented as instruction code executed on a processor device. Further, a Viterbi algorithm may be employed for use in non-uniform level/value assignments.
摘要翻译：提出了用于压缩与特征空间相关联的变换的方法。例如，用于压缩与特征空间相关联的变换的方法包括获得包括多个变换参数的变换，将多个变换参数的多个量化级别中的每一个分配给多个量化值中的一个，以及分配所述多个变换参数中的每一个变换为分配了所述多个量化级中的一个的所述多个量化值之一。获得变换，分配多个量化级别中的每一个以及每个变换参数的分配中的一个或多个被实现为在处理器设备上执行的指令代码。此外，维特比算法可用于非均匀级/值分配中。

13. 发明授权

US08121840B2 System and method for likelihood computation in multi-stream HMM based speech recognition 有权
标题翻译：用于基于多流HMM的语音识别中的似然计算的系统和方法
公开(公告)号：US08121840B2
公开(公告)日：2012-02-21
申请号：US12131190
申请日：2008-06-02
申请人： Stephen Mingyu Chu , Vaibhava Goel , Etienne Marcheret , Gerasimos Potamianos
发明人： Stephen Mingyu Chu , Vaibhava Goel , Etienne Marcheret , Gerasimos Potamianos
IPC分类号： G10L15/14
CPC分类号： G10L15/144
摘要： A system and method for speech recognition includes determining active Gaussians related to a first feature stream and a second feature stream by labeling at least one of the first and second streams, and determining active Gaussians co-occurring in the first stream and the second stream based upon joint probability. A number of Gaussians computed is reduced based upon Gaussians already computed for the first stream and a number of Gaussians co-occurring in the second stream. Speech is decoded based on the Gaussians computed for the first and second streams.
摘要翻译：用于语音识别的系统和方法包括：通过标记第一和第二流中的至少一个来确定与第一特征流和第二特征流相关的活动高斯，以及确定在第一流和第二流中共存的活动高斯联合概率。基于已经为第一个流计算的高斯和在第二个流中共同出现的高斯数，减少了计算出的高斯数。基于为第一和第二流计算的高斯解码语音。

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