Techniques for utterance grouping and for improved training of machine learning models using grouped utterance data

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

• embedding a first plurality of audio samples into a plurality of embeddings, wherein each embedding is a feature representation of a respective portion of the first plurality of audio samples;
• grouping the plurality of embeddings into a plurality of groups, wherein the first plurality of audio samples is embedded using a plurality of embedding models, wherein the embedding and grouping are performed over a plurality of iterations, further comprising:
• at each iteration of the plurality of iterations, updating at least one embedding model of the plurality of embedding models;
• selecting a second plurality of audio samples to be labeled based on the plurality of groups, wherein the second plurality of audio samples is selected based on a plurality of confidence scores for the plurality of groups and based on an embedding space coverage for each of the plurality of groups;
• and
• labeling the selected second plurality of audio samples by applying an automated speech recognition model to the second plurality of audio samples.

A system for training a machine learning model using sample labeling, comprising:

• a processing circuitry;
• and
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• embed a first plurality of audio samples into a plurality of embeddings, wherein each embedding is a feature representation of a respective portion of the first plurality of audio samples;
• group the plurality of embeddings into a plurality of groups, wherein the first plurality of audio samples is embedded using a plurality of embedding models, wherein embedding and grouping are performed over a plurality of iterations, wherein the system is further configured to:
• at each iteration of the plurality of iterations, update at least one embedding model of the plurality of embedding models;
• select a second plurality of audio samples to be labeled based on the plurality of groups, wherein the second plurality of audio samples is selected based on a plurality of confidence scores for the plurality of groups and based on an embedding space coverage for each of the plurality of groups;
• and
• label the selected second plurality of audio samples by applying an automated speech recognition model to the second plurality of audio samples.