Jian Gao Wu

US Patent:

7693713, Apr 6, 2010

Filed:

Jun 17, 2005

Appl. No.:

11/156106

Inventors:

Xiaodong He - Issaquah WA, US
Jian Wu - Redmond WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G10L 15/06

US Classification:

704243, 704245

Abstract:

Speech models are trained using one or more of three different training systems. They include competitive training which reduces a distance between a recognized result and a true result, data boosting which divides and weights training data, and asymmetric training which trains different model components differently.

US Patent:

7925505, Apr 12, 2011

Filed:

Apr 10, 2007

Appl. No.:

11/784896

Inventors:

Jian Wu - Redmond WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G10L 15/06
G10L 15/14

US Classification:

704236, 704244, 704255, 7042563

Abstract:

Architecture is disclosed herewith for minimizing an empirical error rate by discriminative adaptation of a statistical language model in a dictation and/or dialog application. The architecture allows assignment of an improved weighting value to each term or phrase to reduce empirical error. Empirical errors are minimized whether a user provides correction results or not based on criteria for discriminatively adapting the user language model (LM)/context-free grammar (CFG) to the target. Moreover, algorithms are provided for the training and adaptation processes of LM/CFG parameters for criteria optimization.

US Patent:

8185389, May 22, 2012

Filed:

Dec 16, 2008

Appl. No.:

12/335558

Inventors:

Dong Yu - Kirkland WA, US
Li Deng - Redmond WA, US
Yifan Gong - Sammamish WA, US
Jian Wu - Redmond WA, US
Alejandro Acero - Bellevue WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G10L 15/20

US Classification:

704233

Abstract:

Described is noise reduction technology generally for speech input in which a noise-suppression related gain value for the frame is determined based upon a noise level associated with that frame in addition to the signal to noise ratios (SNRs). In one implementation, a noise reduction mechanism is based upon minimum mean square error, Mel-frequency cepstra noise reduction technology. A high gain value (e. g. , one) is set to accomplish little or no noise suppression when the noise level is below a threshold low level, and a low gain value set or computed to accomplish large noise suppression above a threshold high noise level. A noise-power dependent function, e. g. , a log-linear interpolation, is used to compute the gain between the thresholds. Smoothing may be performed by modifying the gain value based upon a prior frame's gain value. Also described is learning parameters used in noise reduction via a step-adaptive discriminative learning algorithm.

US Patent:

8239195, Aug 7, 2012

Filed:

Sep 23, 2008

Appl. No.:

12/235748

Inventors:

Jinyu Li - Redmond WA, US
Li Deng - Redmond WA, US
Dong Yu - Kirkland WA, US
Jian Wu - Sammamish WA, US
Yifan Gong - Sammamish WA, US
Alejandro Acero - Bellevue WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G10L 15/20

US Classification:

704233, 704226, 704256

Abstract:

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 of the compressed model based at least in part upon the received distorted speech utterance.

US Patent:

8354105, Jan 15, 2013

Filed:

Sep 29, 2009

Appl. No.:

12/569830

Inventors:

Jian Wu - Lynnwood WA, US
Nicole Le - Camarillo CA, US
Michael De La Cruz - Camarillo CA, US
Gregory Flynn - Thousand Oaks CA, US

Assignee:

Amgen Inc. - Thousand Oaks CA

International Classification:

A61K 39/00
C07K 16/00
C12P 21/04

US Classification:

4241421, 53038815, 435 703

Abstract:

The present invention relates to methods of modulating (e. g. , reducing) the mannose content, particularly high-mannose content of recombinant glycoproteins.

US Patent:

8532991, Sep 10, 2013

Filed:

Mar 10, 2010

Appl. No.:

12/720968

Inventors:

Xiaodong He - Issaquah WA, US
Jian Wu - Redmond WA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G10L 15/06
G10L 15/00

US Classification:

704243, 704255

Abstract:

Speech models are trained using one or more of three different training systems. They include competitive training which reduces a distance between a recognized result and a true result, data boosting which divides and weights training data, and asymmetric training which trains different model components differently.

US Patent:

20070190057, Aug 16, 2007

Filed:

Dec 22, 2006

Appl. No.:

11/644345

Inventors:

Jian Wu - Lynnwood WA, US
Nicole Le - Camarillo CA, US
Michael De La Cruz - Camarillo CA, US
Gregory Flynn - Thousand Oaks CA, US

International Classification:

A61K 39/395
C12P 21/06
C12N 5/06
C07K 16/24

US Classification:

4241451, 435 691, 435335, 4353201, 53038823, 530395

Abstract:

The present invention relates to methods of modulating (e.g., reducing) the mannose content, particularly high-mannose content of recombinant glycoproteins.

US Patent:

20090325154, Dec 31, 2009

Filed:

Jun 20, 2006

Appl. No.:

11/922385

Inventors:

Jingyue Ju - Englewood Cliffs NJ, US
Jian Wu - New York NY, US
Dae H. Kim - New York NY, US

Assignee:

The Trustees of Columbia University in the City of New York - New York NY

International Classification:

C12Q 1/68
C07H 21/04

US Classification:

435 6, 536 221

Abstract:

This invention provides methods for pyrosequencing and compositions comprising 3′-O— modified deoxynucleoside triphosphates.