Michael I Levit

US Patent:

8180641, May 15, 2012

Filed:

Sep 29, 2008

Appl. No.:

12/240143

Inventors:

Michael Levit - Mountain View CA, US
Shuangyu Chang - Fremont CA, US
Bruce Melvin Buntschuh - Mountain View CA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G10L 15/28

US Classification:

704255, 704252, 704257, 704E15021

Abstract:

Sequential speech recognition using two unequal automatic speech recognition (ASR) systems may be provided. The system may provide two sets of vocabulary data. A determination may be made as to whether entries in one set of vocabulary data are likely to be confused with entries in the other set of vocabulary data. If confusion is likely, a decoy entry from one set of the vocabulary data may be placed in the other set of vocabulary data to ensure more efficient and accurate speech recognition processing may take place.

US Patent:

20100312546, Dec 9, 2010

Filed:

Jun 4, 2009

Appl. No.:

12/477918

Inventors:

Shuangyu Chang - Fremont CA, US
Michael Levit - San Jose CA, US
Bruce Buntschuh - Mountain View CA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G10L 15/18

US Classification:

704 9, 704E15019

Abstract:

Architecture that employs an overall grammar as a set of context-specific grammars for recognition of an input, each responsible for a specific context, such as subtask category, geographic region, etc. The grammars together cover the entire domain. Moreover, multiple recognitions can be run in parallel against the same input, where each recognition uses one or more of the context-specific grammars. The multiple intermediate recognition results from the different recognizer-grammars are reconciled by running re-recognition using a dynamically composed grammar based on the multiple recognition results and potentially other domain knowledge, or selecting the winner using a statistical classifier operating on classification features extracted from the multiple recognition results and other domain knowledge.

US Patent:

20120109652, May 3, 2012

Filed:

Oct 27, 2010

Appl. No.:

12/912938

Inventors:

Michael Levit - San Jose CA, US
Bruce Melvin Buntschuh - Mountain View CA, US

Assignee:

MICROSOFT CORPORATION - Redmond WA

International Classification:

G10L 15/00

US Classification:

704251, 704E15001

Abstract:

On a computing device a speech utterance is received from a user. The speech utterance is a section of a speech dialog that includes a plurality of speech utterances. One or more features from the speech utterance are identified. Each identified feature from the speech utterance is a specific characteristic of the speech utterance. One or more features from the speech dialog are identified. Each identified feature from the speech dialog is associated with one or more events in the speech dialog. The one or more events occur prior to the speech utterance. One or more identified features from the speech utterance and one or more identified features from the speech dialog are used to calculate a confidence score for the speech utterance.

US Patent:

20130080150, Mar 28, 2013

Filed:

Sep 23, 2011

Appl. No.:

13/243098

Inventors:

Michael Levit - San Jose CA, US
Shuangyu Chang - Fremont CA, US
Bruce Melvin Buntschuh - Mountain View CA, US
Nick Kibre - Redwood City CA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G06F 17/27

US Classification:

704 9

Abstract:

A semantic error rate calculation may be provided. After receiving a spoken query from a user, the spoken query may be converted to text according to a first speech recognition hypothesis. A plurality of results associated with the converted query may be received and compared to a second plurality of results associated with the converted query.

US Patent:

20130080162, Mar 28, 2013

Filed:

Sep 23, 2011

Appl. No.:

13/242912

Inventors:

Shuangyu Chang - Fremont CA, US
Michael Levit - San Jose CA, US
Bruce Melvin Buntschuh - Mountain View CA, US

Assignee:

Microsoft Corporation - Redmond WA

International Classification:

G10L 15/26

US Classification:

704235, 704E15043

Abstract:

Query history expansion may be provided. Upon receiving a spoken query from a user, an adapted language model may be applied to convert the spoken query to text. The adapted language model may comprise a plurality of queries interpolated from the user's previous queries and queries associated with other users. The spoken query may be executed and the results of the spoken query may be provided to the user.

US Patent:

20220392432, Dec 8, 2022

Filed:

Jun 8, 2021

Appl. No.:

17/342505

Inventors:

- Redmond WA, US
Tasos ANASTASAKOS - San Jose CA, US
Michael LEVIT - Mountain View CA, US
Nitin AGARWAL - Fremont CA, US

International Classification:

G10L 15/01
G10L 15/22
G10L 15/183
G10L 15/06
G06N 3/08

Abstract:

Systems and methods for speech recognition correction include receiving a voice recognition input from an individual user and using a trained error correction model to add a new alternative result to a results list based on the received voice input processed by a voice recognition system. The error correction model is trained using contextual information corresponding to the individual user. The contextual information comprises a plurality of historical user correction logs, a plurality of personal class definitions, and an application context. A re-ranker re-ranks the results list with the new alternative result and a top result from the re-ranked results list is output.

US Patent:

20200349920, Nov 5, 2020

Filed:

Jun 27, 2019

Appl. No.:

16/454621

Inventors:

- Redmond WA, US
Anand U DESAI - Sunnyvale CA, US
Cem AKSOYLAR - Kirkland WA, US
Michael LEVIT - San Jose CA, US
Xin MENG - Sunnyvale CA, US
Shuangyu CHANG - Sunnyvale CA, US
Suyash CHOUDHURY - Kirkland WA, US
Dhiresh RAWAL - Redmond WA, US
Tao LI - Redmond WA, US
Rishi GIRISH - Seattle WA, US
Marcus JAGER - Boulder Creek CO, US
Ananth Rampura SHESHAGIRI RAO - Sunnyvale CA, US

International Classification:

G10L 15/06
G10L 15/183
G10L 15/14
G06N 20/00

Abstract:

Provided is a system and method for acquiring training data and building an organizational-based language model based on the training data. In one example, the method may include collecting organizational data that is generated via one or more applications associated with an organization, aggregating the collected organizational data with previously collected organizational data to generate aggregated organizational training data, training an organizational-based language model for speech processing based on the aggregated organizational training data, and storing the trained organizational-based language model.

US Patent:

20170092275, Mar 30, 2017

Filed:

Dec 13, 2016

Appl. No.:

15/377267

Inventors:

- Redmond WA, US
Michael Levit - San Jose CA, US
Abhik Lahiri - Mountain View CA, US
Barlas Oguz - Fremont CA, US
Benoit Dumoulin - Palo Alto CA, US

Assignee:

Microsoft Technology Licensing, LLC - Redmond WA

International Classification:

G10L 15/32
G10L 15/06
G10L 15/14

Abstract:

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.