Patents Assigned to ILLUMA Labs Inc.
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Patent number: 11783841Abstract: A method and system for secure speaker authentication between a caller device and a first device using an authentication server are provided. The system comprises extracting features into a feature matrix from an incoming audio call; generating a partial i-vector, wherein the partial i-vector includes a first low-order statistic; sending the partial i-vector to the authentication server; and receiving from the authentication server a match score generated based on a full i-vector and another i-vector being stored on the authentication server, wherein the full i-vector is generated from the partial i-vector.Type: GrantFiled: March 15, 2021Date of Patent: October 10, 2023Assignee: ILLUMA LABS INC.Inventor: Milind Borkar
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Patent number: 11699445Abstract: A system and method for improving T-matrix training for speaker recognition, comprising receiving an audio input, divisible into a plurality of audio frames including at least an audio sample of a human speaker; generating for each audio frame a feature vector; generating for a first plurality of feature vectors centered statistics of at least a zero order and a first order; generating a first i-vector, the first i-vector representing the human speaker; and generating an optimized T-matrix training sequence computation, based on at least the first i-vector.Type: GrantFiled: March 15, 2021Date of Patent: July 11, 2023Assignee: ILLUMA LABS INC.Inventor: Milind Borkar
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Patent number: 11521622Abstract: A system and method for efficient universal background model (UBM) training for speaker recognition, including: receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame of the plurality of audio frames includes an audio sample having a length above a first threshold extracting at least one identifying feature from the first audio frame and generating a feature vector based on the at least one identifying feature; generating an optimized training sequence computation based on the feature vector and a Gaussian Mixture Model (GMM), wherein the GMM is associated with a plurality of components, wherein each of the plurality of components is defined by a covariance matrix, a mean vector, and a weight vector; and updating any of the associated components of the GMM based on the generated optimized training sequence computation.Type: GrantFiled: October 27, 2020Date of Patent: December 6, 2022Assignee: ILLUMA Labs Inc.Inventor: Milind Borkar
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Publication number: 20210201917Abstract: A system and method for improving T-matrix training for speaker recognition, comprising receiving an audio input, divisible into a plurality of audio frames including at least an audio sample of a human speaker; generating for each audio frame a feature vector; generating for a first plurality of feature vectors centered statistics of at least a zero order and a first order; generating a first i-vector, the first i-vector representing the human speaker; and generating an optimized T-matrix training sequence computation, based on at least the first i-vector.Type: ApplicationFiled: March 15, 2021Publication date: July 1, 2021Applicant: ILLUMA Labs Inc.Inventor: Milind BORKAR
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Publication number: 20210201919Abstract: A method and system for secure speaker authentication between a caller device and a first device using an authentication server are provided. The system comprises extracting features into a feature matrix from an incoming audio call; generating a partial i-vector, wherein the partial i-vector includes a first low-order statistic; sending the partial i-vector to the authentication server; and receiving from the authentication server a match score generated based on a full i-vector and another i-vector being stored on the authentication server, wherein the full i-vector is generated from the partial i-vector.Type: ApplicationFiled: March 15, 2021Publication date: July 1, 2021Applicant: ILLUMA Labs Inc.Inventor: Milind BORKAR
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Patent number: 10950243Abstract: A system and method for improving T-matrix training for speaker recognition are provided. The method includes receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame includes an audio sample of a human speaker, the sample having a length above a first threshold; generating for each audio frame a feature vector; generating for a first plurality of feature vectors centered statistics of at least a zero order and a first order; generating a first i-vector, the first i-vector representing the human speaker; generating an optimized T-matrix training sequence computation, based on the first i-vector, an initialized T-matrix, the centered statistics, and a Gaussian mixture model (GMM) of a trained universal background model (UBM).Type: GrantFiled: March 1, 2019Date of Patent: March 16, 2021Assignee: ILLUMA Labs Inc.Inventor: Milind Borkar
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Publication number: 20210043215Abstract: A system and method for efficient universal background model (UBM) training for speaker recognition, including: receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame of the plurality of audio frames includes an audio sample having a length above a first threshold extracting at least one identifying feature from the first audio frame and generating a feature vector based on the at least one identifying feature; generating an optimized training sequence computation based on the feature vector and a Gaussian Mixture Model (GMM), wherein the GMM is associated with a plurality of components, wherein each of the plurality of components is defined by a covariance matrix, a mean vector, and a weight vector; and updating any of the associated components of the GMM based on the generated optimized training sequence computation.Type: ApplicationFiled: October 27, 2020Publication date: February 11, 2021Applicant: ILLUMA Labs Inc.Inventor: Milind BORKAR
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Publication number: 20190244622Abstract: A system and method for enrolling a speaker in a speaker authentication and identification system (AIS), the method comprising: generating a user account, the user account comprising: a user identifier based on one or more metadata elements associated with an audio input received from an end device; generating a first i-vector from an audio frame of the audio input, a trained T-matrix, and a Universal Background Model (UBM), wherein the first i-vector generation comprises an optimized computation; and associating the user account with the first i-vector.Type: ApplicationFiled: April 16, 2019Publication date: August 8, 2019Applicant: ILLUMA Labs Inc.Inventor: Milind BORKAR
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Publication number: 20190198025Abstract: A system and method for improving T-matrix training for speaker recognition are provided. The method includes receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame includes an audio sample of a human speaker, the sample having a length above a first threshold; generating for each audio frame a feature vector; generating for a first plurality of feature vectors centered statistics of at least a zero order and a first order; generating a first i-vector, the first i-vector representing the human speaker; generating an optimized T-matrix training sequence computation, based on the first i-vector, an initialized T-matrix, the centered statistics, and a Gaussian mixture model (GMM) of a trained universal background model (UBM).Type: ApplicationFiled: March 1, 2019Publication date: June 27, 2019Applicant: ILLUMA Labs Inc.Inventor: Milind BORKAR
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Publication number: 20190164557Abstract: A system and method for efficient universal background model (UBM) training for speaker recognition, including: receiving an audio input, divisible into a plurality of audio frames, wherein at least a first audio frame of the plurality of audio frames includes an audio sample having a length above a first threshold extracting at least one identifying feature from the first audio frame and generating a feature vector based on the at least one identifying feature; generating an optimized training sequence computation based on the feature vector and a Gaussian Mixture Model (GMM), wherein the GMM is associated with a plurality of components, wherein each of the plurality of components is defined by a covariance matrix, a mean vector, and a weight vector; and updating any of the associated components of the GMM based on the generated optimized training sequence computation.Type: ApplicationFiled: November 28, 2018Publication date: May 30, 2019Applicant: ILLUMA Labs Inc.Inventor: Milind BORKAR