Cache-based streaming speaker diarization

A method for updating a neural network for streaming diarization, comprising:

• providing a training speech episode and a corresponding ground truth mapping;
• arranging the ground truth mapping into a sequence of arrival-order labels where a first-arriving speaker is assigned a first index and one or more subsequent speakers are assigned a respective subsequent index in order of initial appearance in the training speech episode;
• and
• adjusting parameters of the speaker diarization model to minimize a loss function that penalizes deviations from the sequence of arrival-order labels.

A system comprising:

• one or more processors to:
• process, using a trained speaker diarization model, an input comprising (i) one or more digital units (DUs) of a segment of speech and (ii) a speaker cache of one or more previously processed DUs of the speech to obtain a plurality of likelihood values, each likelihood value characterizing a likelihood of one or more speakers speaking in the segment of the speech, wherein the speaker cache is mapped to an order of appearance, in the speech, of one or more previously detected speakers;
• update the speaker cache using at least:
• the one or more DUs of the segment of the speech, and
• the plurality of likelihood values;
• and
• generate, using the plurality of likelihood values, a speaker-to-time mapping for the speech.