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1. 发明申请

WO2022261808A1 CONTEXTUAL SPELLING CORRECTION (CSC) FOR AUTOMATIC SPEECH RECOGNITION (ASR) 审中-公开
公开(公告)号：WO2022261808A1
公开(公告)日：2022-12-22
申请号：PCT/CN2021/099993
申请日：2021-06-15
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC , WANG, Xiaoqiang , LIU, Yanqing , ZHAO, Sheng , LI, Jinyu
发明人： WANG, Xiaoqiang , LIU, Yanqing , ZHAO, Sheng , LI, Jinyu
IPC分类号： G10L15/18 , G06F40/295
摘要： Novel solutions for speech recognition provide contextual spelling correction (CSC) for automatic speech recognition (ASR). Disclosed examples include receiving an audio stream; performing an ASR process on the audio stream to produce an ASR hypothesis; receiving a context list; and, based on at least the ASR hypothesis and the context list, performing spelling correction to produce an output text sequence. A contextual spelling correction (CSC) model is used on top of an ASR model, precluding the need for changing the original ASR model. This permits run-time user customization based on contextual data, even for large-size context lists. Some examples include filtering ASR hypotheses for the audio stream and, based on at least the ASR hypotheses filtering, determining whether to trigger spelling correction for the ASR hypothesis. Some examples include generating text to speech (TTS) audio using preprocessed transcriptions with context phrases to train the CSC model.

2. 发明申请

WO2021206804A1 SEQUENCE-TO-SEQUENCE SPEECH RECOGNITION WITH LATENCY THRESHOLD 审中-公开
公开(公告)号：WO2021206804A1
公开(公告)日：2021-10-14
申请号：PCT/US2021/018079
申请日：2021-02-15
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： GAUR, Yashesh , LI, Jinyu , LU, Liang , INAGUMA, Hirofumi , GONG, Yifan
IPC分类号： G10L15/16 , G10L15/32 , G10L15/06
摘要： A computing system including one or more processors configured to receive an audio input. The one or more processors may generate a text transcription of the audio input at a sequence-to-sequence speech recognition model, which may assign a respective plurality of external-model text tokens to a plurality of frames included in the audio input. Each external-model text token may have an external-model alignment within the audio input. Based on the audio input, the one or more processors may generate a plurality of hidden states. Based on the plurality of hidden states, the one or more processors may generate a plurality of output text tokens. Each output text token may have a corresponding output alignment within the audio input. For each output text token, a latency between the output alignment and the external-model alignment may be below a predetermined latency threshold. The one or more processors may output the text transcription.

3. 发明申请

WO2021201970A1 GENERATING AND USING TEXT-TO-SPEECH DATA FOR SPEECH RECOGNITION MODELS 审中-公开
公开(公告)号：WO2021201970A1
公开(公告)日：2021-10-07
申请号：PCT/US2021/016492
申请日：2021-02-04
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： YE, Guoli , HUANG, Yan , WEI, Wenning , HE, Lei , SHARMA, Eva , WU, Jian , TIAN, Yao , LIN, Edward C. , GONG, Yifan , ZHAO, Rui , LI, Jinyu , GALE, William Maxwell
IPC分类号： G10L15/065 , G10L15/16 , G10L13/02 , G10L13/08 , G10L25/30 , G10L15/08 , G10L15/063 , G10L15/26 , G10L2015/088
摘要： Systems, methods, and devices are provided for generating and using text-to-speech (TTS) data for improved speech recognition models. A main model is trained with keyword independent baseline training data. In some instances, acoustic and language model sub-components of the main model are modified with new TTS training data. In some instances, the new TTS training is obtained from a multi-speaker neural TTS system for a keyword that is underrepresented in the baseline training data. In some instances, the new TTS training data is used for pronunciation learning and normalization of keyword dependent confidence scores in keyword spotting (KWS) applications. In some instances, the new TTS training data is used for rapid speaker adaptation in speech recognition models.

4. 发明申请

WO2020214260A1 UNIVERSAL ACOUSTIC MODELING USING NEURAL MIXTURE MODELS 审中-公开
公开(公告)号：WO2020214260A1
公开(公告)日：2020-10-22
申请号：PCT/US2020/020718
申请日：2020-03-03
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： DAS, Amit , LI, Jinyu , LIU, Changliang , GONG, Yifan
IPC分类号： G10L15/16 , G10L15/32 , G06N3/02 , G06N20/20
摘要： According to some embodiments, a universal modeling system may include a plurality of domain expert models to each receive raw input data (e.g., a stream of audio frames containing speech utterances) and provide a domain expert output based on the raw input data. A neural mixture component may then generate a weight corresponding to each domain expert model based on information created by the plurality of domain expert models (e.g., hidden features and/or row convolution). The weights might be associated with, for example, constrained scalar numbers, unconstrained scaler numbers, vectors, matrices, etc. An output layer may provide a universal modeling system output (e.g., an automatic speech recognition result) based on each domain expert output after being multiplied by the corresponding weight for that domain expert model.

5. 发明申请

WO2016025355A1 FALSE FACE REPRESENTATION IDENTIFICATION 审中-公开
标题翻译：虚假的表示标识
公开(公告)号：WO2016025355A1
公开(公告)日：2016-02-18
申请号：PCT/US2015/044405
申请日：2015-08-10
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： CHU, Chun-Te , CONRAD, Michael J. , WU, Dijia , LI, Jinyu
IPC分类号： G06K9/00
CPC分类号： G06K9/00255 , G06K9/00221 , G06K9/00248 , G06K9/00268 , G06K9/00281 , G06K9/00288 , G06K9/00899 , G06K9/2018 , G06K9/4652 , G06K9/52 , G06T7/40 , G06T7/50 , G06T7/60 , G06T7/90
摘要： Systems and methods for identifying a false representation of a human face are provided. In one example, a method for identifying a false representation of a human face includes receiving a plurality of different data streams captured by a respective plurality of sensors of differing sensor types sensing a candidate face. In a cascading plurality of stages, one or more of the different data streams are analyzed, wherein each of the stages comprises a different analysis. In one of the cascading plurality of stages, the method determines that one or more of the different data streams corresponds to a false representation of the human face. Based on determining that one or more of the different data streams corresponds to a false representation of a human face, an indication of the false representation is outputted.
摘要翻译：提供了用于识别人脸的虚假表示的系统和方法。在一个示例中，用于识别人脸的虚假表示的方法包括接收由感测候选面部的不同传感器类型的相应多个传感器捕获的多个不同数据流。在级联多个级中，分析一个或多个不同的数据流，其中每个级包括不同的分析。在级联多个级之一中，该方法确定一个或多个不同的数据流对应于人脸的虚假表示。基于确定一个或多个不同数据流对应于人脸的虚假表示，输出虚假表示的指示。

6. 发明申请

WO2022225610A1 OBJECT DATA STORAGE 审中-公开
公开(公告)号：WO2022225610A1
公开(公告)日：2022-10-27
申请号：PCT/US2022/017856
申请日：2022-02-25
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： THOMAS, William Louis , LI, Jinyu , CHEN, Yang , HAN OPPENLANDER, Youyou , BOWLES, Steven John , LIN, Qingfen
IPC分类号： G06V40/16 , G06K9/62 , G06V10/94 , G06F16/27
摘要： The disclosure herein describes systems and methods for object data storage. In some examples, the method includes generating a profile for an object in a directory, the profile including a first feature vector corresponding to the object and a global unique identifier (GUID) corresponding to the first feature vector in the profile; generating a search scope, the search scope including at least the GUID corresponding to the profile; generating a second feature vector from a live image scan; matching the generated second feature vector from the live image scan to the first feature vector using the generated search scope; identifying the GUID corresponding to the first feature vector that matches the second feature vector; and outputting information corresponding to the object of the profile identified by the GUID corresponding to the first feature vector.

7. 发明申请

WO2021015879A1 STREAMING CONTEXTUAL UNIDIRECTIONAL MODELS 审中-公开
公开(公告)号：WO2021015879A1
公开(公告)日：2021-01-28
申请号：PCT/US2020/036820
申请日：2020-06-09
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： LI, Jinyu , AGARWAL, Amit, Kumar , GONG, Yifan , KESAVAMOORTHY, Harini
IPC分类号： G06N3/04 , G10L15/16 , G06N3/08 , G10L15/02
摘要： Streaming machine learning unidirectional models is facilitated by the use of embedding vectors. Processing blocks in the models apply embedding vectors as input. The embedding vectors utilize context of future data (e.g., data that is temporally offset into the future within a data stream) to improve the accuracy of the outputs generated by the processing blocks. The embedding vectors cause a temporal shift between the outputs of the processing blocks and the inputs to which the outputs correspond. This temporal shift enables the processing blocks to apply the embedding vector inputs from processing blocks that are associated with future data.

8. 发明申请

WO2020214281A1 CODE-SWITCHING SPEECH RECOGNITION WITH END-TO-END CONNECTIONIST TEMPORAL CLASSIFICATION MODEL 审中-公开
公开(公告)号：WO2020214281A1
公开(公告)日：2020-10-22
申请号：PCT/US2020/021931
申请日：2020-03-11
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： LI, Jinyu , YE, Guoli , ZHAO, Rui , GONG, Yifan , LI, Ke
IPC分类号： G10L15/16 , G10L15/00 , G06N3/04
摘要： A CS CTC model may be initialed from a major language CTC model by keeping network hidden weights and replacing output tokens with a union of major and secondary language output tokens. The initialized model may be trained by updating parameters with training data from both languages, and a LID model may also be trained with the data. During a decoding process for each of a series of audio frames, if silence dominates a current frame then a silence output token may be emitted. If silence does not dominate the frame, then a major language output token posterior vector from the CS CTC model may be multiplied with the LID major language probability to create a probability vector from the major language. A similar step is performed for the secondary language, and the system may emit an output token associated with the highest probability across all tokens from both languages.

9. 发明申请

WO2017070920A1 SPOOFED FACE DETECTION 审中-公开
标题翻译： SPOOFED脸部检测
公开(公告)号：WO2017070920A1
公开(公告)日：2017-05-04
申请号：PCT/CN2015/093334
申请日：2015-10-30
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC , LI, Jinyu , WEN, Fang , WEI, Yichen , CONRAD, Michael John , CHU, Chun-Te , JAWAID, Aamir
发明人： LI, Jinyu , WEN, Fang , WEI, Yichen , CONRAD, Michael John , CHU, Chun-Te , JAWAID, Aamir
IPC分类号： G06K9/00
CPC分类号： G06K9/00899 , G06K9/4652 , G06K9/6269
摘要： Examples are disclosed herein that relate to detecting spoofed human faces. One example provides a computing device comprising a processor configured to compute a first feature distance between registered image data of a human face in a first spectral region and test image data of the human face in the first spectral region, compute a second feature distance between the registered image data and test image data of the human face in a second spectral region, compute a test feature distance between the test image data in the first spectral region and the test image data in the second spectral region, determine, based on a predetermined relationship, whether the human face to which the test image data in the first and second spectral regions corresponds is a real human face or a spoofed human face, and modify a behavior of the computing device.
摘要翻译：这里公开了与检测伪造的人脸有关的示例。一个示例提供了一种计算设备，该计算设备包括处理器，该处理器被配置为计算第一光谱区域中的人脸的登记图像数据与第一光谱区域中的人脸的测试图像数据之间的第一特征距离，计算第二特征距离在第二光谱区域中记录人脸的登记图像数据和测试图像数据，计算第一光谱区域中的测试图像数据和第二光谱区域中的测试图像数据之间的测试特征距离，基于预定关系，第一和第二光谱区域中的测试图像数据所对应的人脸是否是真人脸或欺骗人脸，并且修改计算设备的行为。

10. 发明申请

WO2015134294A1 LOW-FOOTPRINT ADAPTATION AND PERSONALIZATION FOR A DEEP NEURAL NETWORK 审中-公开
标题翻译：用于深层神经网络的低自适应和个性化
公开(公告)号：WO2015134294A1
公开(公告)日：2015-09-11
申请号：PCT/US2015/017872
申请日：2015-02-27
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC
发明人： XUE, Jian , LI, Jinyu , YU, Dong , SELTZER, Michael L. , GONG, Yifan
IPC分类号： G10L15/07 , G10L15/16
CPC分类号： G10L15/16 , G06N3/082 , G10L15/075
摘要： The adaptation and personalization of a deep neural network (DNN) model for automatic speech recognition is provided. An utterance which includes speech features for one or more speakers may be received in ASR tasks such as voice search or short message dictation. A decomposition approach may then be applied to an original matrix in the DNN model. In response to applying the decomposition approach, the original matrix may be converted into multiple new matrices which are smaller than the original matrix. A square matrix may then be added to the new matrices. Speaker-specific parameters may then be stored in the square matrix. The DNN model may then be adapted by updating the square matrix. This process may be applied to all of a number of original matrices in the DNN model. The adapted DNN model may include a reduced number of parameters than those received in the original DNN model.
摘要翻译：提供了一种用于自动语音识别的深层神经网络（DNN）模型的适应和个性化。可以在诸如语音搜索或短消息听写的ASR任务中接收包括用于一个或多个扬声器的语音特征的话语。然后可以将分解方法应用于DNN模型中的原始矩阵。响应于应用分解方法，原始矩阵可以被转换成小于原始矩阵的多个新矩阵。然后可以将正方形矩阵添加到新矩阵。然后可以将扬声器特定参数存储在方阵中。然后可以通过更新方阵来适应DNN模型。该过程可以应用于DNN模型中的所有原始矩阵。适应的DNN模型可以包括与原始DNN模型中接收的参数相比减少的参数数量。

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