  Speaker embedding models that utilize neural networks to map utterances to a
space where distances reflect similarity between speakers have driven recent
progress in the speaker recognition task. However, there is still a significant
performance gap between recognizing speakers in the training set and unseen
speakers. The latter case corresponds to the few-shot learning task, where a
trained model is evaluated on unseen classes. Here, we optimize a speaker
embedding model with prototypical network loss (PNL), a state-of-the-art
approach for the few-shot image classification task. The resulting embedding
model outperforms the state-of-the-art triplet loss based models in both
speaker verification and identification tasks, for both seen and unseen
speakers.
