Method for reduced computation of t-matrix training for speaker recognition

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

• 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), wherein the GMM is associated with a plurality of components, each defined by a covariance matrix, a mean vector, and a weight, and wherein generating the optimized T-matrix training sequence further comprises:
• 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 improving T-matrix 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 includes an audio sample of a human speaker, the sample having a length above a first threshold;
• generate for each audio frame a feature vector;
• generate for a first plurality of feature vectors centered statistics of at least a zero order and a first order;
• generate a first i-vector, the first i-vector representing the human speaker;
• generate 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), wherein the GMM is associated with a plurality of components, each defined by a covariance matrix, a mean vector, and a weight, and wherein the system is further configured to:
• generate a feature matrix based on the feature vector;
• generate a GMM mean matrix based on the plurality of mean vectors associated with the plurality of GMM components;
• and
• generate a delta matrix based on the feature matrix and the GMM mean matrix.