Narendra K Gupta

US Patent:

6868142, Mar 15, 2005

Filed:

Jan 10, 2001

Appl. No.:

09/757454

Inventors:

Narendra K. Gupta - Dayton NJ, US
Benjamin J. Stern - Morris Township NJ, US

Assignee:

AT&T Corp. - New York NY

International Classification:

H04M001/64
H04M011/10

US Classification:

379 8804, 379 8823, 4554121, 455413

Abstract:

An arrangement for allowing “hands-free” access to DTMF-controlled systems, such as one's voice mail messaging systems, utilizes a speech-to-DTMF tone application that monitors the communication between the user and the DTMF-controlled system. A speech recognition unit is utilized to retrieve certain voice commands (e. g. , “next”, “skip”, “repeat”, “forward”, etc. ) when uttered by the user. The application then translates the received commands into the proper DTMF tone sequence used by the DTMF-controlled system and transmits the DTMF tones to the system. The application is particularly useful in the cell phone environment and avoids the necessity of the user to constantly switch between using the keypad and listening to messages/commands from the system.

US Patent:

7152029, Dec 19, 2006

Filed:

May 31, 2002

Appl. No.:

10/160461

Inventors:

Hiyan Alshawi - Madison NJ, US
Giuseppe DiFabbrizio - Summit NJ, US
Narendra K. Gupta - Dayton NJ, US
Mazin G. Rahim - Matawan NJ, US
Robert E. Schapire - Maplewood NJ, US
Yoram Singer - Murray Hill NJ, US

Assignee:

AT&T Corp. - New York NY

International Classification:

G06F 17/20

US Classification:

704 1

Abstract:

A system for understanding entries, such as speech, develops a classifier by employing prior knowledge with which a given corpus of training entries is enlarged threefold. A rule is created for each of the labels employed in the classifyier, and the created rules are applied to the given corpus to create a corpus of attachments by appending a weight of ηp(x), or 1−ηp(x), to labels of entries that meet, or fail to meet, respectively, conditions of the labels' rules, and to also create a corpus of non-attachments by appending a weight of 1−ηp(x), or ηp(x), to labels of entries that meet, or fail to meet conditions of the labels' rules.

US Patent:

7197460, Mar 27, 2007

Filed:

Dec 19, 2002

Appl. No.:

10/326692

Inventors:

Narendra K. Gupta - Dayton NJ, US
Mazin G Rahim - Matawan NJ, US
Giuseppe Riccardi - Hoboken NJ, US

Assignee:

AT&T Corp. - New York NY

International Classification:

G10L 11/00
G10L 21/00

US Classification:

7042701, 704275, 704251

Abstract:

A voice-enabled help desk service is disclosed. The service comprises an automatic speech recognition module for recognizing speech from a user, a spoken language understanding module for understanding the output from the automatic speech recognition module, a dialog management module for generating a response to speech from the user, a natural voices text-to-speech synthesis module for synthesizing speech to generate the response to the user, and a frequently asked questions module. The frequently asked questions module handles frequently asked questions from the user by changing voices and providing predetermined prompts to answer the frequently asked question.

US Patent:

7295981, Nov 13, 2007

Filed:

Jan 9, 2004

Appl. No.:

10/755014

Inventors:

Narendra K. Gupta - Dayton NJ, US
Mazin G. Rahim - Warren NJ, US
Gokhan Tur - Morris Plains NJ, US
Antony Van der Mude - Hackettstown NJ, US

Assignee:

AT&T Corp. - New York NY

International Classification:

G10L 15/18

US Classification:

704257, 704 9, 704270

Abstract:

A method of generating a natural language model for use in a spoken dialog system is disclosed. The method comprises using sample utterances and creating a number of hand crafted rules for each call-type defined in a labeling guide. A first NLU model is generated and tested using the hand crafted rules and sample utterances. A second NLU model is built using the sample utterances as new training data and using the hand crafted rules. The second NLU model is tested for performance using a first batch of labeled data. A series of NLU models are built by adding a previous batch of labeled data to training data and using a new batch of labeling data as test data to generate the series of NLU models with training data that increases constantly. If not all the labeling data is received, the method comprises repeating the step of building a series of NLU models until all labeling data is received. After all the training data is received, at least once, the method comprises building a third NLU model using all the labeling data, wherein the third NLU model is used in generating the spoken dialog service.

US Patent:

7328146, Feb 5, 2008

Filed:

Jul 11, 2006

Appl. No.:

11/484120

Inventors:

Hiyan Alshawi - Madison NJ, US
Giuseppe DiFabrizzio - Summit NJ, US
Narendra K. Gupta - Dayton NJ, US
Mazin G. Rahim - Matawan NJ, US
Robert E. Schapire - Maplewood NJ, US
Yoram Singer - Murray Hill NJ, US

Assignee:

AT&T Corp. - New York NY

International Classification:

G06F 17/20

US Classification:

704 1, 704 9, 706 25

Abstract:

A system for understanding entries, such as speech, develops a classifier by employing prior knowledge with which a given corpus of training entries is enlarged threefold. A rule is created for each of the labels employed in the classifier, and the created rules are applied to the given corpus to create a corpus of attachments by appending a weight of ηp(x), or 1−ηp(x), to labels of entries that meet, or fail to meet, respectively, conditions of the labels' rules, and to also create a corpus of non-attachments by appending a weight of 1−ηp(x), or ηp(x), to labels of entries that meet, or fail to meet conditions of the labels' rules.

US Patent:

7366655, Apr 29, 2008

Filed:

Apr 2, 2003

Appl. No.:

10/405858

Inventors:

Narendra K. Gupta - Dayton NJ, US
Barbara B. Hollister - Mountainside NJ, US
Mazin G Rahim - Warren NJ, US
Giuseppe Riccardi - Hoboken NJ, US

Assignee:

AT&T Corp. - New York NY

International Classification:

G06F 17/27
G06F 17/21

US Classification:

704 9, 704 10

Abstract:

A method is disclosed for designing a labeling guide for use by a labeler in labeling data used for training a spoken language understanding (SLU) module for an application. The method comprises a labeling guide designer selecting domain-independent actions applicable to an application, selecting domain-dependent objects according to characteristics of the application, and generating a labeling guide using the selected domain-independent actions and selected domain-dependent objects. An advantage of the labeling guide generated in this manner is that the labeling guide designer can easily port the labeling guide to a new application by selecting a set of domain-independent action and then selecting the domain-dependent objects related to the new application.

US Patent:

7451089, Nov 11, 2008

Filed:

Feb 15, 2007

Appl. No.:

11/675166

Inventors:

Narendra K. Gupta - Dayton NJ, US
Mazin G. Rahim - Matawan NJ, US
Giuseppe Riccardi - Hoboken NJ, US

Assignee:

AT&T Intellectual Property II, L.P. - New York NY

International Classification:

G10L 11/00
G10L 21/00

US Classification:

7042701, 704251, 704275

Abstract:

A voice-enabled help desk service is disclosed. The service comprises an automatic speech recognition module for recognizing speech from a user, a spoken language understanding module for understanding the output from the automatic speech recognition module, a dialog management module for generating a response to speech from the user, a natural voices text-to-speech synthesis module for synthesizing speech to generate the response to the user, and a frequently asked questions module. The frequently asked questions module handles frequently asked questions from the user by changing voices and providing predetermined prompts to answer the frequently asked question.

US Patent:

7620550, Nov 17, 2009

Filed:

Oct 3, 2007

Appl. No.:

11/866685

Inventors:

Narendra K. Gupta - Dayton NJ, US
Mazin G. Rahim - Warren NJ, US
Gokhan Tur - Morris Plains NJ, US
Antony Van der Mude - Hackettstown NJ, US

Assignee:

AT&T Intellectual Property II, L.P. - New York NY

International Classification:

G10L 15/18

US Classification:

704257, 704 9, 704270, 704246, 704254, 709228

Abstract:

A method of generating a natural language model for use in a spoken dialog system is disclosed. The method comprises using sample utterances and creating a number of hand crafted rules for each call-type defined in a labeling guide. A first NLU model is generated and tested using the hand crafted rules and sample utterances. A second NLU model is built using the sample utterances as new training data and using the hand crafted rules. The second NLU model is tested for performance using a first batch of labeled data. A series of NLU models are built by adding a previous batch of labeled data to training data and using a new batch of labeling data as test data to generate the series of NLU models with training data that increases constantly. If not all the labeling data is received, the method comprises repeating the step of building a series of NLU models until all labeling data is received. After all the training data is received, at least once, the method comprises building a third NLU model using all the labeling data, wherein the third NLU model is used in generating the spoken dialog service.