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Jinyu Li patents

Recent patents with Jinyu Li listed as an inventor - additional entries may be under other spellings.


Jinyu Li - Related organizations: Microsoft Technology Licensing, Llc patents, Microsoft Corporation patents

Small-footprint deep neural network

10/20/16 - 20160307095 - Conversion of a large-footprint DNN to a small-print DNN is performed using a variety of techniques, including split-vector quantization. The small-foot print DNN may be distributed to a variety of devices, including mobile devices. Further, the small-footprint DNN may aid a digital assistant on a device in interpreting speech input.
Inventors: Jinyu Li, Yifan Gong, Yongqiang Wang

Variable-component deep neural network for robust speech recognition

09/22/16 - 20160275947 - Systems and methods for speech recognition incorporating environmental variables are provided. The systems and methods capture speech to be recognized. The speech is then recognized utilizing a variable component deep neural network (DNN). The variable component DNN processes the captured speech by incorporating an environment variable. The environment variable may
Inventors: Jinyu Li, Rui Zhao, Yifan Gong

Learning student dnn via output distribution

03/17/16 - 20160078339 - Systems and methods are provided for generating a DNN classifier by “learning” a “student” DNN model from a larger more accurate “teacher” DNN model. The student DNN may be trained from un-labeled training data because its supervised signal is obtained by passing the un-labeled training data through the teacher DNN.
Inventors: Jinyu Li, Rui Zhao, Jui-ting Huang, Yifan Gong

Shared hidden layer combination for speech recognition systems

10/29/15 - 20150310858 - Providing a framework for merging automatic speech recognition (ASR) systems having a shared deep neural network (DNN) feature transformation is provided. A received utterance may be evaluated to generate a DNN-derived feature from the top hidden layer of a DNN. The top hidden layer output may then be utilized to
Inventors: Jinyu Li, Jian Xue, Yifan Gong

Low-footprint adaptation and personalization for a deep neural network

09/10/15 - 20150255061 - 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
Inventors: Jian Xue, Jinyu Li, Dong Yu, Michael L. Seltzer, Yifan Gong

Restructuring deep neural network acoustic models

12/18/14 - 20140372112 - A Deep Neural Network (DNN) model used in an Automatic Speech Recognition (ASR) system is restructured. A restructured DNN model may include fewer parameters compared to the original DNN model. The restructured DNN model may include a monophone state output layer in addition to the senone output layer of the
Inventors: Jian Xue, Emilian Stoimenov, Jinyu Li, Yifan Gong

Posterior-based feature with partial distance elimination for speech recognition

09/11/14 - 20140257814 - A high-dimensional posterior-based feature with partial distance elimination may be utilized for speech recognition. The log likelihood values of a large number of Gaussians are needed to generate the high-dimensional posterior feature. Gaussians with very small log likelihoods are associated with zero posterior values. Log likelihoods for Gaussians for a
Inventors: Jinyu Li, Zhijie Yan, Qiang Huo, Yifan Gong

Multilingual deep neural network

09/11/14 - 20140257805 - Described herein are various technologies pertaining to a multilingual deep neural network (MDNN). The MDNN includes a plurality of hidden layers, wherein values for weight parameters of the plurality of hidden layers are learned during a training phase based upon training data in terms of acoustic raw features for multiple
Inventors: Jui-ting Huang, Jinyu Li, Dong Yu, Li Deng, Yifan Gong

Exploiting heterogeneous data in deep neural network-based speech recognition systems

09/11/14 - 20140257804 - Technologies pertaining to training a deep neural network (DNN) for use in a recognition system are described herein. The DNN is trained using heterogeneous data, the heterogeneous data including narrowband signals and wideband signals. The DNN, subsequent to being trained, receives an input signal that can be either a wideband
Inventors: Jinyu Li, Dong Yu, Yifan Gong

Utilizing scalar operations for recognizing utterances during automatic speech recognition in noisy environments

03/06/14 - 20140067387 - Scalar operations for model adaptation or feature enhancement may be utilized for recognizing an utterance during automatic speech recognition in a noisy environment. An utterance including distorted speech generated from a transmission source for delivery to a receiver, may be received by a computer. The distorted speech may be caused
Inventors: Jinyu Li, Michael Lewis Seltzer, Yifan Gong

Online distorted speech estimation within an unscented transformation framework

05/24/12 - 20120130710 - Noise and channel distortion parameters in the vectorized logarithmic or the cepstral domain for an utterance may be estimated, and subsequently the distorted speech parameters in the same domain may be updated using an unscented transformation framework during online automatic speech recognition. An utterance, including speech generated from a transmission
Inventors: Deng Li, Jinyu Li, Dong Yu, Yifan Gong

Emulating legacy hardware using ieee 754 compliant hardware

08/26/10 - 20100217579 - Emulating legacy hardware using IEEE 754 compliant hardware is disclosed herein. In some aspects, the emulation includes locating an instruction that includes NaN (not a number) as at least one of an operand or a resultant. The emulation adjusts the resultant of the instruction, via additional code, to produce a
Inventors: Jinyu Li, Ke Deng, Chen Li

Vgpu: a real time gpu emulator

08/26/10 - 20100214301 - An exemplary method for emulating a graphics processing unit (GPU) includes executing a graphics application on a host computing system to generate commands for a target GPU wherein the host computing system includes host system memory and a different, host GPU; converting the generated commands into intermediate commands; based on
Inventors: Jinyu Li, Chen Li, Gang Chen, Xin Tong

Method for tessellation on graphics hardware

08/26/10 - 20100214294 - An exemplary method for tessellating a primitive of a graphical object includes receiving information for a primitive of a graphical object where the information includes vertex information and an edge factor for each edge of the primitive; based on the received information, dividing the primitive into parts where each part
Inventors: Chen Li, Jinyu Li, Xin Tong

Shader-based finite state machine frame detection

08/12/10 - 20100201691 - Embodiments for shader-based finite state machine frame detection for implementing alternative graphical processing on an animation scenario are disclosed. In accordance with one embodiment, the embodiment includes assigning an identifier to each shader used to render animation scenarios. The embodiment also includes defining a finite state machine for a key
Inventors: Jinyu Li, Chen Li, Xin Tong

Content based cache for graphics resource management

07/29/10 - 20100188412 - Providing content based cache for graphic resource management is disclosed herein. In some aspects, a portion of a shadow copy of graphics resources is updated from an original copy of the graphics resources when a requested resource is not current. The shadow copy may be dedicated to a graphics processing
Inventors: Chen Li, Jinyu Li, Xin Tong, Barry C. Bond, Gang Chen

Single-pass bounding box calculation

07/29/10 - 20100188404 - Embodiments for single-pass bounding box calculation are disclosed. In accordance with one embodiment, the single-pass bounding box calculation includes rendering a first target to a 2-dimensional screen space, whereby the first target includes at least six pixels. The calculation further includes producing transformed vertices in a set of geometry primitives
Inventors: Xin Tong, Chen Li, Jinyu Li

Phase sensitive model adaptation for noisy speech recognition

03/25/10 - 20100076758 - A speech recognition system described herein includes a receiver component that receives a distorted speech utterance. The speech recognition also includes an updater component that is in communication with a first model and a second model, wherein the updater component automatically updates parameters of the second model based at least
Inventors: Jinyu Li, Li Deng, Dong Yu, Yifan Gong, Alejandro Acero

Adapting a compressed model for use in speech recognition

03/25/10 - 20100076757 - A speech recognition system includes a receiver component that receives a distorted speech utterance. The speech recognition also includes an adaptor component that selectively adapts parameters of a compressed model used to recognize at least a portion of the distorted speech utterance, wherein the adaptor component selectively adapts the parameters
Inventors: Jinyu Li, Li Deng, Dong Yu, Jian Wu, Yifan Gong, Alejandro Acero


### Jinyu Li patent invention listings

The bibliographic references displayed about Jinyu Li's patents are for a recent sample of Jinyu Li's publicly published patent applications. The inventor/author may have additional bibliographic citations listed at the USPTO.gov. FreshPatents.com is not associated or affiliated in any way with the author/inventor or the United States Patent/Trademark Office but is providing this non-comprehensive sample listing for educational and research purposes using public bibliographic data published and disseminated from the United States Patent/Trademark Office public datafeed. This information is also available for free on the USPTO.gov website. If Jinyu Li filed recent patent applications under another name, spelling or location then those applications could be listed on an alternate page. If no bibliographic references are listed here, it is possible there are no recent filings or there is a technical issue with the listing--in that case, we recommend doing a search on the USPTO.gov website.

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