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1. 发明申请

US20150278191A1 Flexible Schema for Language Model Customization 有权
标题翻译：语言模型定制的灵活模式
公开(公告)号：US20150278191A1
公开(公告)日：2015-10-01
申请号：US14227492
申请日：2014-03-27
申请人： Microsoft Corporation
发明人： Michael Levit , Hernan Guelman , Shuangyu Chang , Sarangarajan Parthasarathy , Benoit Dumoulin
IPC分类号： G06F17/27
CPC分类号： G10L15/183 , G06F17/2755 , G06F17/2785 , G10L15/30
摘要： The customization of language modeling components for speech recognition is provided. A list of language modeling components may be made available by a computing device. A hint may then be sent to a recognition service provider for combining the multiple language modeling components from the list. The hint may be based on a number of different domains. A customized combination of the language modeling components based on the hint may then be received from the recognition service provider.
摘要翻译：提供了用于语音识别的语言建模组件的定制。语言建模组件的列表可以由计算设备提供。然后，可以向识别服务提供商发送提示，以从列表中组合多个语言建模组件。提示可能基于许多不同的域。然后可以从识别服务提供商接收基于提示的语言建模组件的定制组合。

2. 发明申请

US20150269949A1 INCREMENTAL UTTERANCE DECODER COMBINATION FOR EFFICIENT AND ACCURATE DECODING 有权
标题翻译：增强UTTERANCE解码器组合有效和准确的解码
公开(公告)号：US20150269949A1
公开(公告)日：2015-09-24
申请号：US14219642
申请日：2014-03-19
申请人： MICROSOFT CORPORATION
发明人： Shuangyu Chang , Michael Levit , Abhik Lahiri , Barlas Oguz , Benoit Dumoulin
IPC分类号： G10L19/005
CPC分类号： G10L15/32 , G10L15/063 , G10L15/14 , G10L19/005
摘要： An incremental speech recognition system. The incremental speech recognition system incrementally decodes a spoken utterance using an additional utterance decoder only when the additional utterance decoder is likely to add significant benefit to the combined result. The available utterance decoders are ordered in a series based on accuracy, performance, diversity, and other factors. A recognition management engine coordinates decoding of the spoken utterance by the series of utterance decoders, combines the decoded utterances, and determines whether additional processing is likely to significantly improve the recognition result. If so, the recognition management engine engages the next utterance decoder and the cycle continues. If the accuracy cannot be significantly improved, the result is accepted and decoding stops. Accordingly, a decoded utterance with accuracy approaching the maximum for the series is obtained without decoding the spoken utterance using all utterance decoders in the series, thereby minimizing resource usage.
摘要翻译：增量语音识别系统。只有当附加话语解码器可能对组合结果增加显着的益处时，增量语音识别系统才会使用附加话音解码器递增地解码语音话语。可用的话语解码器是基于准确性，性能，多样性等因素进行排序的。识别管理引擎通过一系列话音解码器来协调语音发音的解码，组合解码的话语，并确定附加处理是否可能显着改善识别结果。如果是这样，识别管理引擎接合下一个话音解码器，并且该周期继续。如果精度无法显着提高，结果被接受，解码停止。因此，在使用系列中的所有话语解码器对语音发音进行解码的情况下，获得具有接近该系列的最大值的精确解码语音，从而最小化资源使用。

3. 发明申请

US20140365218A1 LANGUAGE MODEL ADAPTATION USING RESULT SELECTION 审中-公开
标题翻译：使用结果选择语言模式适应
公开(公告)号：US20140365218A1
公开(公告)日：2014-12-11
申请号：US13913032
申请日：2013-06-07
申请人： Microsoft Corporation
发明人： Shuangyu Chang , Michael Levit
IPC分类号： G10L15/065 , G10L15/06 , G10L15/02
CPC分类号： G10L15/065 , G10L15/02 , G10L15/063 , G10L15/19 , G10L15/32
摘要： A received utterance is recognized using different language models. For example, recognition of the utterance is independently performed using a baseline language model (BLM) and using an adapted language model (ALM). A determination is made as to what results from the different language model are more likely to be accurate. Different features may be used to assist in making the determination (e.g. language model scores, recognition confidences, acoustic model scores, quality measurements, . . . ) may be used. A classifier may be trained and then used in determining whether to select the results using the BLM or to select the results using the ALM. A language model may be automatically trained or re-trained that adjusts a weight of the training data used in training the model in response to differences between the two results obtained from applying the different language models.
摘要翻译：使用不同的语言模型识别接收的话语。例如，使用基准语言模型（BLM）和使用适应语言模型（ALM）来独立地执行语音的识别。确定不同语言模型的结果更有可能是准确的。可以使用不同的特征来帮助确定（例如，语言模型得分，识别信心，声学模型得分，质量测量等）。可以对分类器进行训练，然后用于确定是使用BLM选择结果还是使用ALM选择结果。可以自动训练或重新训练语言模型，以响应于从应用不同语言模型获得的两个结果之间的差异来调整用于训练模型的训练数据的权重。

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