Patents by Inventor Zoltan Tueske

Zoltan Tueske 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: 11183194
    Abstract: Aspects of the present disclosure describe techniques for identifying and recovering out-of-vocabulary words in transcripts of a voice data recording using word recognition models and word sub-unit recognition models. An example method generally includes receiving a voice data recording for transcription into a textual representation of the voice data recording. The voice data recording is transcribed into the textual representation using a word recognition model. An unknown word is identified in the textual representation, and the unknown word is reconstructed based on recognition of sub-units of the unknown word generated by a sub-unit recognition model. The textual representation of the voice data recording is modified by replacing the unknown word with the reconstruction of the unknown word, and the modified textual representation is output.
    Type: Grant
    Filed: September 13, 2019
    Date of Patent: November 23, 2021
    Assignee: International Business Machines Corporation
    Inventors: Samuel Thomas, Kartik Audhkhasi, Zoltan Tueske, Yinghui Huang, Michael Alan Picheny
  • Patent number: 11158303
    Abstract: In an approach to soft-forgetting training, one or more computer processors train a first model utilizing one or more training batches wherein each training batch of the one or more training batches comprises one or more blocks of information. The one or more computer processors, responsive to a completion of the training of the first model, initiate a training of a second model utilizing the one or more training batches. The one or more computer processors jitter a random block size for each block of information for each of the one or more training batches for the second model. The one or more computer processors unroll the second model over one or more non-overlapping contiguous jittered blocks of information. The one or more computer processors, responsive to the unrolling of the second model, reduce overfitting for the second model by applying twin regularization.
    Type: Grant
    Filed: August 27, 2019
    Date of Patent: October 26, 2021
    Assignee: International Business Machines Corporation
    Inventors: Kartik Audhkhasi, George Andrei Saon, Zoltan Tueske, Brian E. D. Kingsbury, Michael Alan Picheny
  • Publication number: 20210183404
    Abstract: Method, apparatus, and computer program product are provided for customizing an automatic closed captioning system. In some embodiments, at a data use (DU) location, an automatic closed captioning system that includes a base model is provided, search criteria are defined to request from one or more data collection (DC) locations, a search request based on the search criteria is sent to the one or more DC locations, relevant closed caption data from the one or more DC locations are received responsive to the search request, the received relevant closed caption data are processed by computing a confidence score for each of a plurality of data sub-sets of the received relevant closed caption data and selecting one or more of the data sub-sets based on the confidence scores, and the automatic closed captioning system is customized by using the selected one or more data sub-sets to train the base model.
    Type: Application
    Filed: December 14, 2019
    Publication date: June 17, 2021
    Inventors: Samuel Thomas, Yinghui Huang, Masayuki Suzuki, Zoltan Tueske, Laurence P. Sansone, Michael A. Picheny
  • Publication number: 20210082437
    Abstract: Aspects of the present disclosure describe techniques for identifying and recovering out-of-vocabulary words in transcripts of a voice data recording using word recognition models and word sub-unit recognition models. An example method generally includes receiving a voice data recording for transcription into a textual representation of the voice data recording. The voice data recording is transcribed into the textual representation using a word recognition model. An unknown word is identified in the textual representation, and the unknown word is reconstructed based on recognition of sub-units of the unknown word generated by a sub-unit recognition model. The textual representation of the voice data recording is modified by replacing the unknown word with the reconstruction of the unknown word, and the modified textual representation is output.
    Type: Application
    Filed: September 13, 2019
    Publication date: March 18, 2021
    Inventors: SAMUEL THOMAS, KARTIK AUDHKHASI, ZOLTAN TUESKE, YINGHUI HUANG, MICHAEL ALAN PICHENY
  • Publication number: 20210065680
    Abstract: In an approach to soft-forgetting training, one or more computer processors train a first model utilizing one or more training batches wherein each training batch of the one or more training batches comprises one or more blocks of information. The one or more computer processors, responsive to a completion of the training of the first model, initiate a training of a second model utilizing the one or more training batches. The one or more computer processors jitter a random block size for each block of information for each of the one or more training batches for the second model. The one or more computer processors unroll the second model over one or more non-overlapping contiguous jittered blocks of information. The one or more computer processors, responsive to the unrolling of the second model, reduce overfitting for the second model by applying twin regularization.
    Type: Application
    Filed: August 27, 2019
    Publication date: March 4, 2021
    Inventors: Kartik Audhkhasi, George Andrei Saon, Zoltan Tueske, Brian E. D. Kingsbury, Michael Alan Picheny