Patents by Inventor Bhuvana Ramabhadran

Bhuvana Ramabhadran has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20180027213
    Abstract: 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.
    Type: Application
    Filed: October 2, 2017
    Publication date: January 25, 2018
    Inventors: STANLEY CHEN, KENNETH W. CHURCH, VAIBHAVA GOEL, LIDIA L. MANGU, ETIENNE MARCHERET, BHUVANA RAMABHADRAN, LAURENCE P. SANSONE, ABHINAV SETHY, SAMUEL THOMAS
  • Publication number: 20170270100
    Abstract: A mechanism is provided in a data processing system for external word embedding neural network language models. The mechanism configures the data processing system with an external word embedding neural network language model that accepts as input a sequence of words and predicts a current word based on the sequence of words. The external word embedding neural network language model combines an external embedding matrix to a history word embedding matrix and a prediction word embedding matrix of the external word embedding neural network language model. The mechanism receives a sequence of input words by the data processing system. The mechanism applies a plurality of previous words in the sequence of input words as inputs to the external word embedding neural network language model. The external word embedding neural network language model generates a predicted current word based on the plurality of previous words.
    Type: Application
    Filed: March 18, 2016
    Publication date: September 21, 2017
    Inventors: Kartik Audhkhasi, Bhuvana Ramabhadran, Abhinav Sethy
  • Publication number: 20170193391
    Abstract: A plurality of corpora is received from one or more sources. A separate model is trained on each corpus of the plurality of corpora. The models for the plurality of corpora are merged into a joint model using parameter interpolation. The models for each corpus of the plurality of corpora are retrained separately using the joint model. A single model is created based on the retrained models.
    Type: Application
    Filed: December 31, 2015
    Publication date: July 6, 2017
    Inventors: Stanley Chen, Bhuvana Ramabhadran, Ebru Arisoy-Saraclar, Abhinav Sethy
  • Publication number: 20170154264
    Abstract: A method, executed by a computer, includes monitoring a conversation between a plurality of meeting participants, identifying a conversational focus within the conversation, generating at least one question corresponding to the conversational focus, and retrieving at least one answer corresponding to the at least one question. A computer system and computer program product corresponding to the method are also disclosed herein.
    Type: Application
    Filed: November 30, 2015
    Publication date: June 1, 2017
    Inventors: Stanley Chen, Kenneth W. Church, Robert G. Farrell, Vaibhava Goel, Lidia L. Mangu, Etienne Marcheret, Michael A. Picheny, Bhuvana Ramabhadran, Laurence P. Sansone, Abhinav Sethy, Samuel Thomas
  • Publication number: 20170150100
    Abstract: 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.
    Type: Application
    Filed: December 6, 2016
    Publication date: May 25, 2017
    Inventors: STANLEY CHEN, KENNETH W. CHURCH, VAIBHAVA GOEL, LIDIA L. MANGU, ETIENNE MARCHERET, BHUVANA RAMABHADRAN, LAURENCE P. SANSONE, ABHINAV SETHY, SAMUEL THOMAS
  • Patent number: 9646001
    Abstract: Operation of an automated dialog system is described using a source language to conduct a real time human machine dialog process with a human user using a target language. A user query in the target language is received and automatically machine translated into the source language. An automated reply of the dialog process is then delivered to the user in the target language. If the dialog process reaches an initial assistance state, a first human agent using the source language is provided to interact in real time with the user in the target language by machine translation to continue the dialog process. Then if the dialog process reaches a further assistance state, a second human agent using the target language is provided to interact in real time with the user in the target language to continue the dialog process.
    Type: Grant
    Filed: September 19, 2011
    Date of Patent: May 9, 2017
    Assignee: Nuance Communications, Inc.
    Inventors: Ruhi Sarikaya, Vaibhava Goel, David Nahamoo, Rèal Tremblay, Bhuvana Ramabhadran, Osamuyimen Stewart
  • Patent number: 9640186
    Abstract: Deep scattering spectral features are extracted from an acoustic input signal to generate a deep scattering spectral feature representation of the acoustic input signal. The deep scattering spectral feature representation is input to a speech recognition engine. The acoustic input signal is decoded based on at least a portion of the deep scattering spectral feature representation input to a speech recognition engine.
    Type: Grant
    Filed: May 2, 2014
    Date of Patent: May 2, 2017
    Assignee: International Business Machines Corporation
    Inventors: Petr Fousek, Vaibhava Goel, Brian E. D. Kingsbury, Etienne Marcheret, Shay Maymon, David Nahamoo, Vijayaditya Peddinti, Bhuvana Ramabhadran, Tara N. Sainath
  • Patent number: 9584758
    Abstract: 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.
    Type: Grant
    Filed: November 25, 2015
    Date of Patent: February 28, 2017
    Assignee: International Business Machines Corporation
    Inventors: Stanley Chen, Kenneth W. Church, Vaibhava Goel, Lidia L. Mangu, Etienne Marcheret, Bhuvana Ramabhadran, Laurence P. Sansone, Abhinav Sethy, Samuel Thomas
  • Patent number: 9542389
    Abstract: Methods and apparatus for language translation in a computing environment associated with a virtual application are presented. For example, a method for providing language translation includes determining languages of a user and a correspondent; determining one or more sequences of translators; determining a selected sequence of selected translators from the one or more sequences of the translators; requesting a change in virtual locations, within the computing environment associated with the virtual application, of one or more selected translator virtual representations of the selected translators to a virtual meeting location within the computing environment associated with the virtual application; and changing virtual locations of the one or more selected translator virtual representations to the virtual meeting location.
    Type: Grant
    Filed: October 25, 2013
    Date of Patent: January 10, 2017
    Assignee: International Business Machines Corporation
    Inventors: Dimitri Kanevsky, Clifford Alan Pickover, Bhuvana Ramabhadran, Irina Rish
  • Patent number: 9524716
    Abstract: Some embodiments relate to using an unnormalized neural network language model in connection with a speech processing application. The techniques include obtaining a language segment sequence comprising one or more language segments in a vocabulary; accessing an unnormalized neural network language model having a normalizer node and an output layer comprising a plurality of output nodes, each of the plurality of output nodes associated with a respective language segment in the vocabulary; and determining, using the unnormalized neural network language model, a first likelihood that a first language segment in the vocabulary follows the language segment sequence.
    Type: Grant
    Filed: April 17, 2015
    Date of Patent: December 20, 2016
    Assignee: Nuance Communications, Inc.
    Inventors: Abhinav Sethy, Stanley Chen, Bhuvana Ramabhadran, Paul J. Vozila
  • Patent number: 9524289
    Abstract: Aspects described herein provide various approaches to annotating text samples in order to construct natural language grammars. A text sample may be selected for annotation. A set of annotation candidates may be generated based on the text sample. A classifier may be used to score the set of annotation candidates in order to obtain a set of annotation scores. One of the annotation candidates may be selected as a suggested annotation for the text sample based on the set of annotation scores. A grammar rule may be derived based on the suggested annotation, and a grammar may be configured to include the annotation-derived grammar rule.
    Type: Grant
    Filed: February 24, 2014
    Date of Patent: December 20, 2016
    Assignee: Nuance Communications, Inc.
    Inventors: Leonid Rachevsky, Raimo Bakis, Bhuvana Ramabhadran
  • Patent number: 9524734
    Abstract: A simulation method and system. A computing system receives a first audio and/or video data stream. The first audio and/or video data stream includes data associated with a first person. The computing system monitors the first audio and/or video data stream. The computing system identifies emotional attributes comprised by the first audio and/or video data stream. The computing system generates a second audio and/or video data stream associated with the first audio and/or video data stream. The second audio and/or video data stream includes the data without the emotional attributes. The computing system stores the second audio and/or video data stream.
    Type: Grant
    Filed: January 21, 2016
    Date of Patent: December 20, 2016
    Assignee: International Business Machines Corporation
    Inventors: Sara H. Basson, Dimitri Kanevsky, Edward Emile Kelley, Bhuvana Ramabhadran
  • Patent number: 9514744
    Abstract: Techniques for conversion of non-back-off language models for use in speech decoders. For example, an apparatus for conversion of non-back-off language models for use in speech decoders. For example, an apparatus is configured convert a non-back-off language model to a back-off language model. The converted back-off language model is pruned. The converted back-off language model is usable for decoding speech.
    Type: Grant
    Filed: August 12, 2013
    Date of Patent: December 6, 2016
    Assignee: International Business Machines Corporation
    Inventors: Ebru Arisoy, Bhuvana Ramabhadran, Abhinav Sethy, Stanley Chen
  • Publication number: 20160343369
    Abstract: Techniques for conversion of non-back-off language models for use in speech decoders. For example, an apparatus for conversion of non-back-off language models for use in speech decoders. For example, an apparatus is configured convert a non-back-off language model to a back-off language model. The converted back-off language model is pruned. The converted back-off language model is usable for decoding speech.
    Type: Application
    Filed: August 1, 2016
    Publication date: November 24, 2016
    Inventors: Ebru Arisoy, Bhuvana Ramabhadran, Abhinav Sethy, Stanley Chen
  • Patent number: 9484023
    Abstract: Techniques for conversion of non-back-off language models for use in speech decoders. For example, a method comprises the following step. A non-back-off language model is converted to a back-off language model. The converted back-off language model is pruned. The converted back-off language model is usable for decoding speech.
    Type: Grant
    Filed: February 22, 2013
    Date of Patent: November 1, 2016
    Assignee: International Business Machines Corporation
    Inventors: Ebru Arisoy, Bhuvana Ramabhadran, Abhinav Sethy, Stanley Chen
  • Publication number: 20160307564
    Abstract: Some embodiments relate to using an unnormalized neural network language model in connection with a speech processing application. The techniques include obtaining a language segment sequence comprising one or more language segments in a vocabulary; accessing an unnormalized neural network language model having a normalizer node and an output layer comprising a plurality of output nodes, each of the plurality of output nodes associated with a respective language segment in the vocabulary; and determining, using the unnormalized neural network language model, a first likelihood that a first language segment in the vocabulary follows the language segment sequence.
    Type: Application
    Filed: April 17, 2015
    Publication date: October 20, 2016
    Applicant: Nuance Communications, Inc.
    Inventors: Abhinav Sethy, Stanley Chen, Bhuvana Ramabhadran, Paul J. Vozila
  • Patent number: 9418334
    Abstract: Pretraining for a DBN initializes weights of the DBN (Deep Belief Network) using a hybrid pre-training methodology. Hybrid pre-training employs generative component that allows the hybrid PT method to have better performance in WER (Word Error Rate) compared to the discriminative PT method. Hybrid pre-training learns weights which are more closely linked to the final objective function, allowing for a much larger batch size compared to generative PT, which allows for improvements in speed; and a larger batch size allows for parallelization of the gradient computation, speeding up training further.
    Type: Grant
    Filed: December 6, 2012
    Date of Patent: August 16, 2016
    Assignee: Nuance Communications, Inc.
    Inventors: Tara N. Sainath, Brian Kingsbury, Bhuvana Ramabhadran
  • Patent number: 9367526
    Abstract: A language processing application employs a classing function optimized for the underlying production application context for which it is expected to process speech. A combination of class based and word based features generates a classing function optimized for a particular production application, meaning that a language model employing the classing function uses word classes having a high likelihood of accurately predicting word sequences encountered by a language model invoked by the production application. The classing function optimizes word classes by aligning the objective of word classing with the underlying language processing task to be performed by the production application. The classing function is optimized to correspond to usage in the production application context using class-based and word-based features by computing a likelihood of a word in an n-gram and a frequency of a word within a class of the n-gram.
    Type: Grant
    Filed: July 26, 2011
    Date of Patent: June 14, 2016
    Assignee: Nuance Communications, Inc.
    Inventors: Paul Vozila, Maximilian Bisani, Yi Su, Stephen M. Chu, Stanley F. Chen, Ruhi Sarikaya, Bhuvana Ramabhadran
  • Publication number: 20160140985
    Abstract: A simulation method and system. A computing system receives a first audio and/or video data stream. The first audio and/or video data stream includes data associated with a first person. The computing system monitors the first audio and/or video data stream. The computing system identifies emotional attributes comprised by the first audio and/or video data stream. The computing system generates a second audio and/or video data stream associated with the first audio and/or video data stream. The second audio and/or video data stream includes the data without the emotional attributes. The computing system stores the second audio and/or video data stream.
    Type: Application
    Filed: January 21, 2016
    Publication date: May 19, 2016
    Inventors: Sara H. Basson, Dimitri Kanevsky, Edward Emile Kelley, Bhuvana Ramabhadran
  • Patent number: 9330661
    Abstract: Techniques disclosed herein include systems and methods for voice-enabled searching. Techniques include a co-occurrence based approach to improve accuracy of the 1-best hypothesis for non-phrase voice queries, as well as for phrased voice queries. A co-occurrence model is used in addition to a statistical natural language model and acoustic model to recognize spoken queries, such as spoken queries for searching a search engine. Given an utterance and an associated list of automated speech recognition n-best hypotheses, the system rescores the different hypotheses using co-occurrence information. For each hypothesis, the system estimates a frequency of co-occurrence within web documents. Combined scores from a speech recognizer and a co-occurrence engine can be combined to select a best hypothesis with a lower word error rate.
    Type: Grant
    Filed: January 16, 2014
    Date of Patent: May 3, 2016
    Assignee: Nuance Communications, Inc.
    Inventors: Jonathan Mamou, Abhinav Sethy, Bhuvana Ramabhadran, Ron Hoory, Paul Joseph Vozila, Nathan Bodenstab