Fully supervised speaker diarization

A system comprising:

• data processing hardware;
• memory hardware in communication with the data processing hardware and storing instructions, that when executed by the data processing hardware, cause the data processing hardware to perform operations comprising:
• receiving audio data corresponding to an utterance of speech;
• segmenting the audio data into a plurality of segments;
• and
• for a current speech segment occurring after an initial speech segment of the plurality of segments:
• extracting a current speaker-discriminative embedding from the current speech segment;
• predicting, using a neural network model configured to receive the current speaker-discriminative embedding extracted from the current speech segment as input, a probability distribution over possible speakers for the current speech segment by:
• for each of one or more existing speakers associated with a corresponding speaker label, predicting a probability that the current speaker-discriminative embedding includes the existing speaker based on a corresponding existing speaker-discriminative embedding that is assigned to the corresponding speaker label associated with the existing speaker;
• and
• predicting a probability that the current speaker-discriminative embedding includes a new speaker;
• and
• based on the probability distribution over possible speakers for the current speech segment, assigning, to the current speaker-discriminative embedding, a current speaker label as one of:
• a new speaker label associated with the new speaker;
• or
• the corresponding speaker label associated with one of the one or more existing speakers.