Patents by Inventor Asela J. R. Gunawardana

Asela J. R. Gunawardana 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).

  • Patent number: 8180640
    Abstract: Described is the use of acoustic data to improve grapheme-to-phoneme conversion for speech recognition, such as to more accurately recognize spoken names in a voice-dialing system. A joint model of acoustics and graphonemes (acoustic data, phonemes sequences, grapheme sequences and an alignment between phoneme sequences and grapheme sequences) is described, as is retraining by maximum likelihood training and discriminative training in adapting graphoneme model parameters using acoustic data. Also described is the unsupervised collection of grapheme labels for received acoustic data, thereby automatically obtaining a substantial number of actual samples that may be used in retraining. Speech input that does not meet a confidence threshold may be filtered out so as to not be used by the retrained model.
    Type: Grant
    Filed: June 20, 2011
    Date of Patent: May 15, 2012
    Assignee: Microsoft Corporation
    Inventors: Xiao Li, Asela J. R. Gunawardana, Alejandro Acero, Jr.
  • Publication number: 20110251844
    Abstract: Described is the use of acoustic data to improve grapheme-to-phoneme conversion for speech recognition, such as to more accurately recognize spoken names in a voice-dialing system. A joint model of acoustics and graphonemes (acoustic data, phonemes sequences, grapheme sequences and an alignment between phoneme sequences and grapheme sequences) is described, as is retraining by maximum likelihood training and discriminative training in adapting graphoneme model parameters using acoustic data. Also described is the unsupervised collection of grapheme labels for received acoustic data, thereby automatically obtaining a substantial number of actual samples that may be used in retraining. Speech input that does not meet a confidence threshold may be filtered out so as to not be used by the retrained model.
    Type: Application
    Filed: June 20, 2011
    Publication date: October 13, 2011
    Applicant: MICROSOFT CORPORATION
    Inventors: Xiao Li, Asela J. R. Gunawardana, Alejandro Acero
  • Patent number: 7991615
    Abstract: Described is the use of acoustic data to improve grapheme-to-phoneme conversion for speech recognition, such as to more accurately recognize spoken names in a voice-dialing system. A joint model of acoustics and graphonemes (acoustic data, phonemes sequences, grapheme sequences and an alignment between phoneme sequences and grapheme sequences) is described, as is retraining by maximum likelihood training and discriminative training in adapting graphoneme model parameters using acoustic data. Also described is the unsupervised collection of grapheme labels for received acoustic data, thereby automatically obtaining a substantial number of actual samples that may be used in retraining. Speech input that does not meet a confidence threshold may be filtered out so as to not be used by the retrained model.
    Type: Grant
    Filed: December 7, 2007
    Date of Patent: August 2, 2011
    Assignee: Microsoft Corporation
    Inventors: Xiao Li, Asela J. R. Gunawardana, Alejandro Acero
  • Publication number: 20090150153
    Abstract: Described is the use of acoustic data to improve grapheme-to-phoneme conversion for speech recognition, such as to more accurately recognize spoken names in a voice-dialing system. A joint model of acoustics and graphonemes (acoustic data, phonemes sequences, grapheme sequences and an alignment between phoneme sequences and grapheme sequences) is described, as is retraining by maximum likelihood training and discriminative training in adapting graphoneme model parameters using acoustic data. Also described is the unsupervised collection of grapheme labels for received acoustic data, thereby automatically obtaining a substantial number of actual samples that may be used in retraining. Speech input that does not meet a confidence threshold may be filtered out so as to not be used by the retrained model.
    Type: Application
    Filed: December 7, 2007
    Publication date: June 11, 2009
    Applicant: MICROSOFT CORPORATION
    Inventors: Xiao Li, Asela J. R. Gunawardana, Alejandro Acero
  • Patent number: 7206741
    Abstract: A speech signal is decoded by determining a production-related value for a current state based on an optimal production-related value at the end of a preceding state, the optimal production-related value being selected from a set of continuous values. The production-related value is used to determine a likelihood of a phone being represented by a set of observation vectors that are aligned with a path between the preceding state and the current state. The likelihood of the phone is combined with a score from the preceding state to determine a score for the current state, the score from the preceding state being associated with a discrete class of production-related values wherein the class matches the class of the optimal production-related value.
    Type: Grant
    Filed: December 6, 2005
    Date of Patent: April 17, 2007
    Assignee: Microsoft Corporation
    Inventors: Li Deng, Jian-lai Zhou, Frank Torsten Bernd Seide, Asela J. R. Gunawardana, Hagai Attias, Alejandro Acero, Xuedong Huang
  • Patent number: 7117153
    Abstract: A method of modeling a speech recognition system includes decoding a speech signal produced from a training text to produce a sequence of predicted speech units. The training text comprises a sequence of actual speech units that is used with the sequence of predicted speech units to form a confusion model. In further embodiments, the confusion model is used to decode a text to identify an error rate that would be expected if the speech recognition system decoded speech based on the text.
    Type: Grant
    Filed: February 13, 2003
    Date of Patent: October 3, 2006
    Assignee: Microsoft Corporation
    Inventors: Milind Mahajan, Yonggang Deng, Alejandro Acero, Asela J. R. Gunawardana, Ciprian Chelba
  • Patent number: 7103544
    Abstract: A method of modeling a speech recognition system includes decoding a speech signal produced from a training text to produce a sequence of predicted speech units. The training text comprises a sequence of actual speech units that is used with the sequence of predicted speech units to form a confusion model. In further embodiments, the confusion model is used to decode a text to identify an error rate that would be expected if the speech recognition system decoded speech based on the text.
    Type: Grant
    Filed: June 6, 2005
    Date of Patent: September 5, 2006
    Assignee: Microsoft Corporation
    Inventors: Milind Mahajan, Yonggang Deng, Alejandro Acero, Asela J. R. Gunawardana, Ciprian Chelba
  • Patent number: 7050975
    Abstract: A method of speech recognition is provided that identifies a production-related dynamics value by performing a linear interpolation between a production-related dynamics value at a previous time and a production-related target using a time-dependent interpolation weight. The hidden production-related dynamics value is used to compute a predicted value that is compared to an observed value of acoustics to determine the likelihood of the observed acoustics given a sequence of hidden phonological units. In some embodiments, the production-related dynamics value at the previous time is selected from a set of continuous values. In addition, the likelihood of the observed acoustics given a sequence of hidden phonological units is combined with a score associated with a discrete class of production-related dynamic values at the previous time to determine a score for a current phonological state.
    Type: Grant
    Filed: October 9, 2002
    Date of Patent: May 23, 2006
    Assignee: Microsoft Corporation
    Inventors: Li Deng, Jian-Iai Zhou, Frank Torsten Bernd Seide, Asela J. R. Gunawardana, Hagai Attias, Alejandro Acero, Xuedong Huang
  • Publication number: 20040162730
    Abstract: A method of modeling a speech recognition system includes decoding a speech signal produced from a training text to produce a sequence of predicted speech units. The training text comprises a sequence of actual speech units that is used with the sequence of predicted speech units to form a confusion model. In further embodiments, the confusion model is used to decode a text to identify an error rate that would be expected if the speech recognition system decoded speech based on the text.
    Type: Application
    Filed: February 13, 2003
    Publication date: August 19, 2004
    Applicant: Microsoft Corporation
    Inventors: Milind Mahajan, Yonggang Deng, Alejandro Acero, Asela J.R. Gunawardana, Ciprian Chelba
  • Publication number: 20040019483
    Abstract: A method of speech recognition is provided that identifies a production-related dynamics value by performing a linear interpolation between a production-related dynamics value at a previous time and a production-related target using a time-dependent interpolation weight. The hidden production-related dynamics value is used to compute a predicted value that is compared to an observed value of acoustics to determine the likelihood of the observed acoustics given a sequence of hidden phonological units. In some embodiments, the production-related dynamics value at the previous time is selected from a set of continuous values. In addition, the likelihood of the observed acoustics given a sequence of hidden phonological units is combined with a score associated with a discrete class of production-related dynamic values at the previous time to determine a score for a current phonological state.
    Type: Application
    Filed: October 9, 2002
    Publication date: January 29, 2004
    Inventors: Li Deng, Jian-Iai Zhou, Frank Torsten Bernd Seide, Asela J.R. Gunawardana, Hagai Attias, Alejandro Acero, Xuedong Huang
  • Patent number: 6571210
    Abstract: A method and system of performing confidence measure in a speech recognition system includes receiving an utterance of input speech and creating a near-miss pattern or a near-miss list of possible word entries for the utterance. Each word entry includes an associated value of probability that the utterance corresponds to the word entry. The near-miss list of possible word entries is compared with corresponding stored near-miss confidence templates. Each word in the vocabulary (or keyword list) of near-miss confidence template, which includes a list of word entries and each word entry in each list includes an associated value. Confidence measure for a particular hypothesis word is performed based on the comparison of the values in the near-miss list of possible word entries with the values of the corresponding near-miss confidence template.
    Type: Grant
    Filed: November 13, 1998
    Date of Patent: May 27, 2003
    Assignee: Microsoft Corporation
    Inventors: Hsiao-Wuen Hon, Asela J. R. Gunawardana
  • Publication number: 20010018654
    Abstract: A method and system of performing confidence measure in a speech recognition system includes receiving an utterance of input speech and creating a near-miss pattern or a near-miss list of possible word entries for the utterance. Each word entry includes an associated value of probability that the utterance corresponds to the word entry. The near-miss list of possible word entries is compared with corresponding stored near-miss confidence templates. Each word in the vocabulary (or keyword list) of near-miss confidence template, which includes a list of word entries and each word entry in each list includes an associated value. Confidence measure for a particular hypothesis word is performed based on the comparison of the values in the near-miss list of possible word entries with the values of the corresponding near-miss confidence template.
    Type: Application
    Filed: November 13, 1998
    Publication date: August 30, 2001
    Inventors: HSIAO-WUEN HON, ASELA J.R. GUNAWARDANA