Patents by Inventor Anusha Lalitha

Anusha Lalitha 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: 20220335929
    Abstract: Various embodiments include methods and devices for implementing automatic grammar augmentation for improving voice command recognition accuracy in systems with a small footprint acoustic model. Alternative expressions that may capture acoustic model decoding variations may be added to a grammar set. An acoustic model-specific statistical pronunciation dictionary may be derived by running the acoustic model through a large general speech dataset and constructing a command-specific candidate set containing potential grammar expressions. Greedy based and cross-entropy-method (CEM) based algorithms may be utilized to search the candidate set for augmentations with improved recognition accuracy.
    Type: Application
    Filed: March 21, 2022
    Publication date: October 20, 2022
    Inventors: Yang YANG, Anusha Lalitha, Jin Won LEE, Christopher LOTT
  • Patent number: 11282512
    Abstract: Various embodiments include methods and devices for implementing automatic grammar augmentation for improving voice command recognition accuracy in systems with a small footprint acoustic model. Alternative expressions that may capture acoustic model decoding variations may be added to a grammar set. An acoustic model-specific statistical pronunciation dictionary may be derived by running the acoustic model through a large general speech dataset and constructing a command-specific candidate set containing potential grammar expressions. Greedy based and cross-entropy-method (CEM) based algorithms may be utilized to search the candidate set for augmentations with improved recognition accuracy.
    Type: Grant
    Filed: October 28, 2019
    Date of Patent: March 22, 2022
    Assignee: Qualcomm Incorporated
    Inventors: Yang Yang, Anusha Lalitha, Jin Won Lee, Christopher Lott
  • Publication number: 20210012196
    Abstract: A method may include training, based on a first training data available at a first node in a network, a first local machine learning model. A first local belief of a parameter set of a global machine learning model may be updated based on the training of the first local machine learning model. A second local belief of the parameter set of the global machine learning model may be received from a second node in the network. The second local belief may have been updated based on the second node training a second local machine learning model. The second local machine learning model may be trained based on a second training data available at the second node. The first local belief may be updated based on the second local belief of the second node. Related systems and articles of manufacture, including computer program products, are also provided.
    Type: Application
    Filed: July 10, 2020
    Publication date: January 14, 2021
    Inventors: Anusha Lalitha, Tara Javidi, Farinaz Koushanfar, Osman Cihan Kilinc
  • Publication number: 20200135179
    Abstract: Various embodiments include methods and devices for implementing automatic grammar augmentation for improving voice command recognition accuracy in systems with a small footprint acoustic model. Alternative expressions that may capture acoustic model decoding variations may be added to a grammar set. An acoustic model-specific statistical pronunciation dictionary may be derived by running the acoustic model through a large general speech dataset and constructing a command-specific candidate set containing potential grammar expressions. Greedy based and cross-entropy-method (CEM) based algorithms may be utilized to search the candidate set for augmentations with improved recognition accuracy.
    Type: Application
    Filed: October 28, 2019
    Publication date: April 30, 2020
    Inventors: Yang Yang, Anusha Lalitha, Jin Won Lee, Christopher Lott