Sample-efficient representation learning for real-time latent speaker state characterization

A system comprising:

• at least one processor;
• and
• a memory storing instructions that, when executed by the at least one processor, cause the system to perform a method comprising:
• providing audio waveform data that corresponds to a voice sample to a temporal convolutional network for evaluation, wherein the temporal convolutional network pre-processes the audio waveform data and outputs an identity embedding associated with the audio waveform data;
• obtaining the identity embedding associated with the voice sample from the temporal convolutional network;
• and
• determining information describing a speaker associated with the voice sample based at least in part on the identity embedding.

A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:

• providing audio waveform data that corresponds to a voice sample to a temporal convolutional network for evaluation, wherein the temporal convolutional network pre-processes the audio waveform data and outputs an identity embedding associated with the audio waveform data;
• obtaining the identity embedding associated with the voice sample from the temporal convolutional network;
• and
• determining information describing a speaker associated with the voice sample based at least in part on the identity embedding.