Dragon Systems, Inc.

Voice Signal Technologies, Inc.

Semantic Machines, Inc.

Microsoft Technology Licensing, LLC

Nuance Communications, Inc.

Apple Inc.

Other

Scansoft, Inc.

Laurence Steven Gillick

Robert S Roth

Daniel Lawrence Roth

Michael Jack Newman

David Leo Wright Hall

James K Baker

Daniel Louis Klein

Jonathan P Yamron

Jordan Rian Cohen

Adam David Pauls

Pengyu Chen

Jesse Daniel Eskes Rusak

Steven A Wegmann

Jordan Cohen

Jacob Daniel Andreas

Paul G Bamberg

Steven Andrew Wegmann

Matthias Paulik

Gunnar Evermann

Michael D Edgington

Andrew Volpe

Janet M Baker

Barbara R Peskin

Taylor Darwin Berg-Kirkpatrick

Paul A Van Mulbregt

Thomas E Lynch

Joel M Gould

Percy Shuo Liang

Jonathan Hood Young

Toffee A Albina

Joshua James Clausman

Igor Zlokarnik

Arkady Khasin

Dean G Sturtevant

Donald R McAllaster

David C Petty

Francesco Scattone

Paul E Rensing

Linda Manganaro

Ashwin Rao

Michael D Bibeault

Venkatesh Nagesha

James Barnett

Andres Corrada-Emmanuel

Alan Walsh

Yoshiko Ito

Charles Clayton Wooters

Mike Shire

Haakon L Chevalier

Jean Crawford

Linda Kathleen Arsenault

Marlboro B Moore, Iii

Jordan R Cohen

A method of automatically delineating turns in a multi-turn dialogue between a user and a conversational computing interface. Audio data encoding speech of the user in the multi-turn dialogue is received. The audio data is analyzed to recognize, in the speech of the user, an utterance followed by a silence. The utterance is recognized as a last utterance in a turn of the multi-turn dialogue responsive to the silence exceeding a context-dependent duration dynamically updated based on a conversation history of the multi-turn dialogue and features of the received audio, wherein the conversation history includes one or more previous turns of the multi-turn dialogue taken by the user and one or more previous turns of the multi-turn dialogue taken by the conversational computing interface.

Automatic turn delineation in multi-turn dialogue

A method for configuring an automated dialogue system uses traces of interactions via a graphical user interface (GUI) for an application. Each trace includes interactions in the context of a plurality of presentations of the GUI. Elements of one or more presentations of the GUI are identified, and templates are associated with portions of the trace. Each template has one or more defined inputs and a defined output. For each template of the plurality of templates, the portions of the traces are processed to automatically configure the template by specifying a procedure for providing values of inputs to the template via the GUI and obtaining a value of an output. The automated dialogue system is configured with the configured templates, thereby avoiding manual configuration of the dialogue system.

Automatic spoken dialogue script discovery

A method for generating a dialogue event in a natural language processing system comprises loading, into a computer memory, a computer-readable seed command describing an ordered sequence of two or more top-level dialogue events. A dialogue event includes a client utterance or a computerized assistant response. The seed command includes one or more sub-commands, each sub-command corresponding to a portion of the ordered sequence of two or more top-level dialogue events, and the focal sub-command of the one or more sub-commands being parametrized by a seed semantic parameter. The method further comprises re-parametrizing the focal sub-command by outputting a plurality of different re-parametrized focal sub-commands wherein, in each re-parametrized focal sub-command, the seed semantic parameter is replaced by one of a plurality of different synthetic semantic parameters. The method further comprises, for each of the plurality of different synthetic semantic parameters: saving a corresponding re-parametrized focal sub-command.

Generating dialogue events for natural language system

A data collection system is based on a general set of dialogue acts which are derived from a database schema. Crowd workers perform two types of tasks: (i) identification of sensical dialogue paths and (ii) performing context-dependent paraphrasing of these dialogue paths into real dialogues. The end output of the system is a set of training examples of real dialogues which have been annotated with their logical forms. This data can be used to train all three components of the dialogue system: (i) the semantic parser for understanding context-dependent utterances, (ii) the dialogue policy for generating new dialogue acts given the current state, and (iii) the generation system for both deciding what to say and how to render it in natural language.

Data collection for a new conversational dialogue system

Systems and processes are disclosed for discovering trending terms in automatic speech recognition. Candidate terms (e.g., words, phrases, etc.) not yet found in a speech recognizer vocabulary or having low language model probability can be identified based on trending usage in a variety of electronic data sources (e.g., social network feeds, news sources, search queries, etc.). When candidate terms are identified, archives of live or recent speech traffic can be searched to determine whether users are uttering the candidate terms in dictation or speech requests. Such searching can be done using open vocabulary spoken term detection to find phonetic matches in the audio archives. As the candidate terms are found in the speech traffic, notifications can be generated that identify the candidate terms, provide relevant usage statistics, identify the context in which the terms are used, and the like.

Method and apparatus for discovering trending terms in speech requests

A method for configuring an automated dialog system uses traces of interactions via a graphical user interface (GUI) for an application. Each trace includes interactions in the context of a plurality of presentations of the GUI. Elements of one or more presentations of the GUI are identified, and templates are associated with portions of the trace. Each template has one or more defined inputs and a defined output. For each template of the plurality of templates, the portions of the traces are processed to automatically configure the template by specifying a procedure for providing values of inputs to the template via the GUI and obtaining a value of an output. The automated dialog system is configured with the configured templates, thereby avoiding manual configuration of the dialog system.

An interaction assistant conducts multiple turn interaction dialogs with a user in which context is maintained between turns, and the system manages the dialog to achieve an inferred goal for the user. The system includes a linguistic interface to a user and a parser for processing linguistic events from the user. A dialog manager of the system is configured to receive alternative outputs from the parser, and selecting an action and causing the action to be performed based on the received alternative outputs. The system further includes a dialog state for an interaction with the user, and the alternative outputs represent alternative transitions from a current dialog state to a next dialog state. The system further includes a storage for a plurality of templates, and wherein each dialog state is defined in terms of an interrelationship of one or more instances of the templates.

Iteratively updating parameters for dialog states

Automated assistant for user interaction via speech

In one aspect, a method of processing a voice signal to extract information to facilitate training a speech synthesis model is provided. The method comprises acts of detecting a plurality of candidate features in the voice signal, performing at least one comparison between one or more combinations of the plurality of candidate features and the voice signal, and selecting a set of features from the plurality of candidate features based, at least in part, on the at least one comparison. In another aspect, the method is performed by executing a program encoded on a computer readable medium. In another aspect, a speech synthesis model is provided by, at least in part, performing the method.

Methods and apparatus for formant-based voice synthesis

A method of generating an alternative pronunciation for a word or phrase, given an initial pronunciation and a spoken example of the word or phrase, includes providing the initial pronunciation of the word or phrase, and generating the alternative pronunciation by searching a neighborhood of pronunciations about the initial pronunciation via a constrained hypothesis, wherein the neighborhood includes pronunciations that differ from the initial pronunciation by at most one phoneme. The method further includes selecting a highest scoring pronunciation within the neighborhood of pronunciations.

Pronunciation discovery for spoken words

Methods and apparatus for formant-based voice systems

A method for speech recognition. The method uses a single pronunciation estimator to train acoustic phoneme models and recognize utterances from multiple languages. The method includes accepting text spellings of training words in a plurality of sets of training words, each set corresponding to a different one of a plurality of languages. The method also includes, for each of the sets of training words in the plurality, receiving pronunciations for the training words in the set, the pronunciations being characteristic of native speakers of the language of the set, the pronunciations also being in terms of subword units at least some of which are common to two or more of the languages. The method also includes training a single pronunciation estimator using data comprising the text spellings and the pronunciations of the training words.

Multilingual speech recognition

Channel normalization for automatic speech recognition is provided. Statistics are measured from an initial portion of a speech utterance. Feature normalization parameters are estimated based on the measured statistics and a statistically derived mapping relating measured statistics and feature normalization parameters. In some examples, the measured statistics comprise measures of an energy from the initial portion of the speech utterance. In some examples, measures of the energy comprise extreme values of the energy.

Automatic speech recognition channel normalization based on measured statistics from initial portions of speech utterances

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Newton, MA, United States of America

Laurence Steven Gillick