Leveraging speaker attribute information using multi task learning for speaker verification and diarization

Understand

Deep speaker embeddings have become the leading method for encoding speaker identity in speaker recognition tasks.

• The embedding space should ideally capture the variations between all possible speakers, encoding the multiple acoustic aspects that make up a speaker's identity, whilst being robust to non-speaker acoustic variation.
• Deep speaker embeddings are normally trained discriminatively, predicting speaker identity labels on the training data.
• We hypothesise that additionally predicting speaker-related auxiliary variables -- such as age and nationality -- may yield representations that are better able to generalise to unseen speakers.