Patents by Inventor Thomas Fang ZHENG

Thomas Fang ZHENG 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).

  • Text dependent speaker recognition with long-term feature based on functional data analysis
    Patent number: 9153235
    Abstract: One or more test features are extracted from a time domain signal. The test features are represented by discrete data. The discrete data is represented for each of the one or more test features by a corresponding one or more fitting functions, which are defined in terms of finite number of continuous basis functions and a corresponding finite number of expansion coefficients. Each fitting function is compressed through Functional Principal Component Analysis (FPCA) to generate corresponding sets of principal components. Each principal component for a given test feature is uncorrelated to each other principal component for the given test feature. A distance between a set of principal components for the given test feature and a set of principal components for one or more training features with the processing system is calculated. The test feature is classified according to the distance calculated with the processing system.
    Type: Grant
    Filed: March 13, 2013
    Date of Patent: October 6, 2015
    Assignee: Sony Computer Entertainment Inc.
    Inventors: Chenhao Zhang, Ruxin Chen, Thomas Fang Zheng
  • TEXT DEPENDENTSPEAKER RECOGNITION WITH LONG-TERM FEATURE BASED ON FUNCTIONAL DATA ANALYSIS
    Publication number: 20130268272
    Abstract: One or more test features are extracted from a time domain signal. The test features are represented by discrete data. The discrete data is represented for each of the one or more test features by a corresponding one or more fitting functions, which are defined in terms of finite number of continuous basis functions and a corresponding finite number of expansion coefficients. Each fitting function is compressed through Functional Principal Component Analysis (FPCA) to generate corresponding sets of principal components. Each principal component for a given test feature is uncorrelated to each other principal component for the given test feature. A distance between a set of principal components for the given test feature and a set of principal components for one or more training features with the processing system is calculated. The test feature is classified according to the distance calculated with the processing system.
    Type: Application
    Filed: March 13, 2013
    Publication date: October 10, 2013
    Applicant: Sony Computer Entertainment Inc.
    Inventors: Chenhao ZHANG, Ruxin CHEN, Thomas Fang ZHENG