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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

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。

3. 发明申请

WO2016037311A1 VARIABLE-COMPONENT DEEP NEURAL NETWORK FOR ROBUST SPEECH RECOGNITION 审中-公开
标题翻译：可变组件深度神经网络，用于强大的语音识别
公开(公告)号：WO2016037311A1
公开(公告)日：2016-03-17
申请号：PCT/CN2014/086121
申请日：2014-09-09
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC , LI, Jinyu , ZHAO, Rui , GONG, Yifan
发明人： LI, Jinyu , ZHAO, Rui , GONG, Yifan
IPC分类号： G10L15/20 , G10L15/06
CPC分类号： G10L15/20 , G10L15/16 , G10L19/24 , G10L25/84
摘要： System and method for speech recognition incorporating environmental variables are provided. The system comprises: a speech capture device (202); a feature extraction module (204); an environment variable module (206), wherein the environment variable module determines a value for an environment variable; and a speech recognition decoder (208), wherein the speech recognition decoder utilizes a deep neural network (DNN) to recognize speech captured by the speech capture device, wherein one or more components of the DNN are modeled as a set of functions of the environment variable.
摘要翻译：提供了包含环境变量的语音识别系统和方法。该系统包括：语音捕获设备（202）; 特征提取模块（204）; 环境变量模块（206），其中所述环境变量模块确定环境变量的值; 和语音识别解码器（208），其中所述语音识别解码器利用深神经网络（DNN）来识别由所述语音捕获设备捕获的语音，其中所述DNN的一个或多个组件被建模为所述环境的一组功能变量。

4. 发明申请

WO2023272466A1 CANONICAL TRAINING FOR HIGHLY CONFIGURABLE MULTILINGUAL SPEECH RECOGNTION 审中-公开
公开(公告)号：WO2023272466A1
公开(公告)日：2023-01-05
申请号：PCT/CN2021/102947
申请日：2021-06-29
申请人： MICROSOFT TECHNOLOGY LICENSING, LLC , LI, Jinyu , ZHOU, Long , SUN, Xie , LIU, Shujie
发明人： LI, Jinyu , ZHOU, Long , SUN, Xie , LIU, Shujie
IPC分类号： G10L15/00 , G10L15/32 , G10L15/16
摘要： Embodiments are provided for building a configurable multilingual model. A computing system obtains a plurality of language-specific automatic speech recognition modules and a universal automatic speech recognition module trained on a multi-language training dataset comprising training data corresponding to each of the plurality of different languages. The computing system then compiles the universal automatic speech recognition module with the plurality of language-specific automatic speech recognition modules to generate a configurable multilingual model that is configured to selectively and dynamically utilize a sub-set of the plurality of language-specific automatic speech recognition modules with the universal automatic speech recognition module to process audio content in response to user input identifying one or more target languages associated with the audio content.

5. 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.

6. 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.

7. 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.

8. 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。

9. 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.

10. 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.

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