System and method for efficient processing of universal background models for speaker recognition

A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to perform a process, the process comprising:

• 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, which includes a first computation, the first computation including:
• generating a feature matrix based on the feature vector;
• generating a GMM mean matrix based on the plurality of mean vectors associated with the plurality of GMM components;
• and generating a delta matrix based on the feature matrix and the GMM mean matrix.

A system for efficient universal background model (UBM) training for speaker recognition, comprising:

• a processing circuitry;
• and
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• receive 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;
• extract at least one identifying feature from the first audio frame and generating a feature vector based on the at least one identifying feature;
• generate 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
• update any of the associated components of the GMM based on the generated optimized training sequence computation, which includes a first computation, wherein the first computation includes generating a feature matrix based on the feature vector;
• generating a GMM mean matrix based on the plurality of mean vectors associated with the plurality of GMM components;
• and generating a delta matrix based on the feature matrix and the GMM mean matrix.