International Business Machines Corporation

Nuance Communications, Inc.

Pryon Incorporated

Vaibhava Goel

Etienne Marcheret

Steven John Rennie

David Nahamoo

Bhuvana Ramabhadran

Samuel Jonathan Thomas

Peder Andreas Olsen

Ellen Eide Kislal

Igor Roditis Jablokov

Hong-Kwang Jeff Kuo

Cheng Wu

Marie Wenzel Meteer

Soonthorn Ativanichayaphong

Xiaodong Cui

Lidia Mangu

Stanley S Chen

Kenneth W Church

Abhinav Sethy

Laurence P Sansone

Yuqing Gao

Ramesh Ambat Gopinath

Brian E D Kingsbury

Sabine V Deligne

Pierre L Dognin

Josef Vopicka

John Pruitt

Chul Sung

Jing Chang Huang

David M Lubensky

Karthik Visweswariah

Ea-Ee Jan

Osamuyimen Stewart

Stephen Mingyu Chu

Gerasimos Potamianos

Ewout Van Den Berg

Andrzej Sakrajda

Petr Fousek

Bryan Dempsey

Antonio R Lee

Petr Novak

Joseph Allen Pruitt

Carmi Rothberg

Ajinkya Jitendra Zadbuke

Dimitri Kanevsky

Tara N Sainath

Takashi Fukuda

Yong Qin

Gregory Pelton

Ellen Marie Eide

Benoit Maison

Ruhi Sarikaya

Dan Jiang

John Randall Hershey

Vijayaditya Peddinti

Rèal Tremblay

Pichappan Pethachi

Jerome L Quinn

Shay Maymon

William J Ablondi

David Claiborn

Lucas Siler

Neil Rohit Mallinar

Chui Sung

Dominique O'Donnell

Dominique O'donnell

A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database. The method further comprises generating a first answer to said first question, wherein generating said first answer comprises causing said question to traverse a first path through said question-answering system, wherein the first path comprises a knowledge base constructed from content of the document collection using a machine learning BERT model; and causing said question to traverse a second path through said question-answering system to generate a second answer to said question, said second answer differing from said first answer, wherein the second path comprises a structured query generator and the database, the database being distinct from the knowledge base.

Question-answering system for answering relational questions by utilizing two paths where at least one path uses BERT model

Disclosed are methods, systems, devices, apparatus, media, and other implementations that include a method for document processing (particularly for training of a machine learning question answering platform, and for ingestion of documents). The method includes obtaining a question dataset (e.g., either from public or private repositories of questions) comprising one or more source questions for document processing by a machine learning question-and-answer system that provides answer data in response to question data submitted by a user, modifying a source question from the question dataset to generate one or more augmented questions with equivalent semantic meanings as that of the source question, and processing a document with the one or more augmented questions.

Methods and systems for improved document processing and information retrieval

Disclosed are methods, systems, devices, apparatus, media, and other implementations, including a method that includes obtaining a query set (e.g., a universal set of questions), performing a question-and-answer (Q-A) search on one or more documents using the query set to produce answer data responsive to one or more questions included in the query set, with the answer data characterizing concepts associated with the one or more documents, and deriving structured output information for the one or more documents based on the answer data produced in response to performing the Q-A search.

Supervised summarization and structuring of unstructured documents

A question-answering system that receive a natural-language question includes a database to provide a basis for that answer and a structured-query generator that constructs a structured query from the question and uses it to obtain an answer to the question from the database.

Visual structure of documents in question answering

Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving a source document, applying one or more pre-processes to the source document to produce contextual information representative of the structure and content of the source document, and transforming the source document, based on the contextual information, to generate a question-and-answer searchable document.

Document pre-processing for question-and-answer searching

Disclosed are methods, systems, devices, apparatus, media, design structures, and other implementations, including a method that includes receiving, at a local device from a remote device, query data representative of a question relating to source content of a source document, and determining whether one or more pre-determined questions stored in a question-answer cache maintained at the local device matches the query data according to one or more matching criteria. The method further includes obtaining from the question-answer cache, in response to a determination that at least one of the pre-determined questions matches the query data received from the remote device, at least one answer data item, associated with at least one pre-determined question, corresponding to an answer to the question relating to the source content.

Systems and methods for question-and-answer searching using a cache

A method of combining data streams from fixed audio-visual sensors with data streams from personal mobile devices including, forming a communication link with at least one of one or more personal mobile devices; receiving at least one of an audio data stream and/or a video data stream from the at least one of the one or more personal mobile devices; determining the quality of the at least one of the audio data stream and/or the video data stream, wherein the audio data stream and/or the video data stream having a quality above a threshold quality is retained; and combining the retained audio data stream and/or the video data stream with the data streams from the fixed audio-visual sensors.

Combining installed audio-visual sensors with ad-hoc mobile audio-visual sensors for smart meeting rooms

Systems and methods for training a neural network to optimize network performance, including sampling an applied dropout rate for one or more nodes of the network to evaluate a current generalization performance of one or more training models. An optimized annealing schedule may be generated based on the sampling, wherein the optimized annealing schedule includes an altered dropout rate configured to improve a generalization performance of the network. A number of nodes of the network may be adjusted in accordance with a dropout rate specified in the optimized annealing schedule. The steps may then be iterated until the generalization performance of the network is maximized.

Annealed dropout training of neural networks

A computer-implemented method according to one embodiment includes estimating a speaker dependent acoustic model utilizing test speech data and maximum likelihood linear regression (MLLR), transforming labeled speech data to create transformed speech data, utilizing the speaker dependent acoustic model and a linear transformation, and adjusting a deep neural network (DNN) acoustic model, utilizing the transformed speech data.

Adjusting a deep neural network acoustic model

A computer-implemented method according to one embodiment includes estimating a speaker dependent acoustic model utilizing test speech data and a hybrid estimation technique, transforming labeled speech data to create transformed speech data, utilizing the speaker dependent acoustic model and a nonlinear transformation, and adjusting a deep neural network (DNN) acoustic model, utilizing the transformed speech data.

Aspects described herein are directed towards methods, computing devices, systems, and computer-readable media that apply scattering operations to extracted visual features of audiovisual input to generate predictions regarding the speech status of a subject. Visual scattering coefficients generated according to one or more aspects described herein may be used as input to a neural network operative to generate the predictions regarding the speech status of the subject. Predictions generated based on the visual features may be combined with predictions based on audio input associated with the visual features. In some embodiments, the extracted visual features may be combined with the audio input to generate a combined feature vector for use in generating predictions.

Audio-visual speech recognition with scattering operators

Methods and apparatus for using visual information to facilitate a speech recognition process. The method comprises dividing received audio information into a plurality of audio frames, determining for each of the plurality of audio frames, whether the audio information in the audio frame comprises speech from the foreground speaker, wherein the determining is based, at least in part, on received visual information, and transmitting the audio frame to an automatic speech recognition (ASR) engine for speech recognition when it is determined that the audio frame comprises speech from the foreground speaker.

Methods and apparatus for speech recognition using visual information

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Chappaqua, NY, United States of America

Vaibhava Goel