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, 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, an initialized T-matrix, the centered statistics, and a Gaussian Mixture Model (GMM) of a trained universal background model (UBM), wherein generating the optimized T-matrix training sequence further comprises:
• generating a first multi-dimensional array comprising a plurality of duplicated matrices, wherein each matrix includes a plurality of GMM mean vectors;
• generating a multi-dimensional feature matrix, comprising a plurality of feature matrices, wherein each feature matrix corresponding to a feature vector of a single audio frame;
• and
• generating a multi-dimensional delta array based on the first multi-dimensional array and the multi-dimensional feature 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 including at least an audio sample of a human speaker;
• 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;
• and
• generate an optimized T-matrix training sequence computation, based on:
• at least 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 generating the optimized T-matrix training sequence further comprises:
• generating a first multi-dimensional array comprising a plurality of duplicated matrices, wherein each matrix includes a plurality of GMM mean vectors;
• generating a multi-dimensional feature matrix, comprising a plurality of feature matrices, wherein each feature matrix corresponding to a feature vector of a single audio frame;
• and
• generating a multi-dimensional delta array based on the first multi-dimensional array and the multi-dimensional feature matrix.