Patents by Inventor Tomas Beran

Tomas Beran 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).

  • Enhancement to Viterbi speech processing algorithm for hybrid speech models that conserves memory
    Patent number: 7805305
    Abstract: The present invention discloses a method for semantically processing speech for speech recognition purposes. The method can reduce an amount of memory required for a Viterbi search of an N-gram language model having a value of N greater than two and also having at least one embedded grammar that appears in a multiple contexts to a memory size of approximately a bigram model search space with respect to the embedded grammar. The method also reduces needed CPU requirements. Achieved reductions can be accomplished by representing the embedded grammar as a recursive transition network (RTN), where only one instance of the recursive transition network is used for the contexts. Other than the embedded grammars, a Hidden Markov Model (HMM) strategy can be used for the search space.
    Type: Grant
    Filed: October 12, 2006
    Date of Patent: September 28, 2010
    Assignee: Nuance Communications, Inc.
    Inventors: Daniel E. Badt, Tomas Beran, Radek Hampl, Pavel Krbec, Jan Sedivy
  • ENHANCEMENT TO VITERBI SPEECH PROCESSING ALGORITHM FOR HYBRID SPEECH MODELS THAT CONSERVES MEMORY
    Publication number: 20080091429
    Abstract: The present invention discloses a method for semantically processing speech for speech recognition purposes. The method can reduce an amount of memory required for a Viterbi search of an N-gram language model having a value of N greater than two and also having at least one embedded grammar that appears in a multiple contexts to a memory size of approximately a bigram model search space with respect to the embedded grammar. The method also reduces needed CPU requirements. Achieved reductions can be accomplished by representing the embedded grammar as a recursive transition network (RTN), where only one instance of the recursive transition network is used for the contexts. Other than the embedded grammars, a Hidden Markov Model (HMM) strategy can be used for the search space.
    Type: Application
    Filed: October 12, 2006
    Publication date: April 17, 2008
    Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Daniel E. Badt, Tomas Beran, Radek Hampl, Pavel Krbec, Jan Sedivy