Brian E Roark, 582215 NW Mill Pond Rd, Portland, OR 97229

7752046, Jul 6, 2010

Oct 29, 2004

10/976378

Michiel A. E. Bacchiani - Summit NJ, US
Brian E. Roark - Morristown NJ, US

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

G10L 15/06

704245

Disclosed are systems and methods for providing a spoken dialog system using meta-data to build language models to improve speech processing. Meta-data is generally defined as data outside received speech; for example, meta-data may be a customer profile having a name, address and purchase history of a caller to a spoken dialog system. The method comprises building tree clusters from meta-data and estimating a language model using the built tree clusters. The language model may be used by various modules in the spoken dialog system, such as the automatic speech recognition module and/or the dialog management module. Building the tree clusters from the meta-data may involve generating projections from the meta-data and further may comprise computing counts as a result of unigram tree clustering and then building both unigram trees and higher-order trees from the meta-data as well as computing node distances within the built trees that are used for estimating the language model.

8069043, Nov 29, 2011

Jun 3, 2010

12/793181

Brian E. Roark - Morristown NJ, US

AT&T Intellectual Property II, L.P. - Atlanta GA

G10L 15/06

704245

Disclosed are systems and methods for providing a spoken dialog system using meta-data to build language models to improve speech processing. Meta-data is generally defined as data outside received speech; for example, meta-data may be a customer profile having a name, address and purchase history of a caller to a spoken dialog system. The method comprises building tree clusters from meta-data and estimating a language model using the built tree clusters. The language model may be used by various modules in the spoken dialog system, such as the automatic speech recognition module and/or the dialog management module. Building the tree clusters from the meta-data may involve generating projections from the meta-data and further may comprise computing counts as a result of unigram tree clustering and then building both unigram trees and higher-order trees from the meta-data as well as computing node distances within the built trees that are used for estimating the language model.

7996224, Aug 9, 2011

Oct 29, 2004

10/977030

Sameer Raj Maskey - New York NY, US
Brian E. Roark - Morristown NJ, US
Richard William Sproat - Urbana IL, US

AT&T Intellectual Property II, L.P. - Atlanta GA

G10L 15/04

704254

Systems and methods relate to generating a language model for use in, for example, a spoken dialog system or some other application. The method comprises building a class-based language model, generating at least one sequence network and replacing class labels in the class-based language model with the at least one sequence network. In this manner, placeholders or tokens associated with classes can be inserted into the models at training time and word/phone networks can be built based on meta-data information at test time. Finally, the placeholder token can be replaced with the word/phone networks at run time to improve recognition of difficult words such as proper names.

2010028, Nov 4, 2010

Jan 12, 2009

12/812401

Deniz Erdogmus - Beaverton OR, US
Brian Roark - Portland OR, US
Jan Van Santen - Lake Oswego OR, US
Michael Pavel - Portland OR, US

A61B 5/0482

600545

Embodiments of the disclosed technology provide reliable and fast communication of a human through a direct brain interface which detects the intent of the user. An embodiment of the disclosed technology comprises a system and method in which least one sequence of a plurality of stimuli is presented to an individual (using appropriate sensory modalities), and the time course of at least one measurable response to the sequence(s) is used to select at least one stimulus from the sequence(s). In an embodiment, the sequence(s) may be dynamically altered based on previously selected stimuli and/or on estimated probability distributions over the stimuli. In an embodiment, such dynamic alteration may be based on predictive models of appropriate sequence generation mechanisms, such as an adaptive or static sequence model.

2020019, Jun 18, 2020

Dec 12, 2019

16/712492

- Mountain View CA, US
Min Ma - New York NY, US
Pedro J. Moreno Mengibar - Jersey City NJ, US
Jesse Emond - New York NY, US
Brian E. Roark - Portland OR, US

G10L 15/19
G10L 15/06
G10L 15/22
G10L 15/16

Methods, systems, and apparatus, including computer programs stored on a computer-readable storage medium, for transliteration for speech recognition training and scoring. In some implementations, language examples are accessed, some of which include words in a first script and words in one or more other scripts. At least portions of some of the language examples are transliterated to the first script to generate a training data set. A language model is generated based on occurrences of the different sequences of words in the training data set in the first script. The language model is used to perform speech recognition for an utterance.

2019022, Jul 18, 2019

Mar 21, 2019

16/360752

- Mountain View CA, US
Bryan Horling - Belmont MA, US
Maryam Garrett - Cambridge MA, US
Brian Roark - Portland OR, US
Richard Sproat - Hamilton NJ, US

G06F 17/28
G06F 16/31
H04L 12/58
G06F 17/27
G06F 17/22

Some implementations are directed to translating chatspeak to a normalized form, where the chatspeak is included in natural language input formulated by a user via a user interface input device of a computing device—such as input provided by the user to an automated assistant. The normalized form of the chatspeak may be utilized by the automated assistant in determining reply content that is responsive to the natural language input, and that reply content may be presented to the user via one or more user interface output devices of the computing device of the user. Some implementations are additionally and/or alternatively directed to providing, for presentation to a user, natural language output that includes chatspeak in lieu of a normalized form of the chatspeak, based at least in part on a “chatspeak measure” that is determined based on past usage of chatspeak by the user and/or by additional users.

2017033, Nov 23, 2017

May 17, 2016

15/157293

- Mountain View CA, US
Bryan Horling - Belmont MA, US
Maryam Garrett - Cambridge MA, US
Brian Roark - Portland OR, US
Richard Sproat - Hamilton NJ, US

G06F 17/28
G06F 17/22
G06F 17/30
H04L 12/58

Some implementations are directed to translating chatspeak to a normalized form, where the chatspeak is included in natural language input formulated by a user via a user interface input device of a computing device—such as input provided by the user to an automated assistant. The normalized form of the chatspeak may be utilized by the automated assistant in determining reply content that is responsive to the natural language input, and that reply content may be presented to the user via one or more user interface output devices of the computing device of the user. Some implementations are additionally and/or alternatively directed to providing, for presentation to a user, natural language output that includes chatspeak in lieu of a normalized form of the chatspeak, based at least in part on a “chatspeak measure” that is determined based on past usage of chatspeak by the user and/or by additional users.

2015026, Sep 24, 2015

Mar 24, 2015

14/667518

- Mountain View CA, US
Brian E. Roark - Portland OR, US

G10L 15/18
G10L 15/26

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, relating to enhanced maximum entropy models. In some implementations, data indicating a candidate transcription for an utterance and a particular context for the utterance are received. A maximum entropy language model is obtained. Feature values are determined for n-gram features and backoff features of the maximum entropy language model. The feature values are input to the maximum entropy language model, and an output is received from the maximum entropy language model. A transcription for the utterance is selected from among a plurality of candidate transcriptions based on the output from the maximum entropy language model. The selected transcription is provided to a client device.