Voice classification in hearing aid

A hearing instrument comprising:

• one or more microphones;
• and
• one or more programmable processors, configured to:
• receive reference audio data representing one or more voices;
• generate, using a first machine learning (ML) model, an embedding of the reference audio data;
• receive live audio data representing sound detected by the one or more microphones;
• generate an input spectrogram of the live audio data;
• use a second ML model to generate a masked spectrogram based on the embedding and the input spectrogram, wherein the masked spectrogram represents a version of the live audio data in which portions of the live audio data spoken in the voices represented by the reference audio data are enhanced;
• and
• cause one or more receivers of the one or more hearing instruments to output sound based on the masked spectrogram.

One or more non-transitory computer-readable media comprising instructions stored thereon that, when executed by one or more processors, configured to cause the one or more processors to:

• receive reference audio data representing one or more voices;
• generate, using a first machine learning (ML) model, an embedding of the reference audio data;
• receive live audio data representing sound detected by one or more microphones of one or more hearing instruments;
• generate an input spectrogram of the live audio data;
• and
• use a second ML model to generate a masked spectrogram based on the embedding and the input spectrogram, wherein the masked spectrogram represents a version of the live audio data in which portions of the live audio data spoken in the voices represented by the reference audio data are enhanced;
• and
• cause one or more receivers of the one or more hearing instruments to output sound based on the masked spectrogram.