Centroid-based Deep Metric Learning for Speaker Recognition

Jixuan Wang; Kuan-Chieh Wang; Marc T. Law; Frank Rudzicz; Michael Brudno

ICASSP 2019accepted0 citations

Centroid-based Deep Metric Learning for Speaker Recognition

Jixuan Wang, Kuan-Chieh Wang, Marc T. Law, Frank Rudzicz, Michael Brudno

Abstract

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.

BibTeX

@inproceedings{icassp2019_centroidbaseddee,
  title = {Centroid-based Deep Metric Learning for Speaker Recognition},
  author = {Jixuan Wang and Kuan-Chieh Wang and Marc T. Law and Frank Rudzicz and Michael Brudno},
  booktitle = {ICASSP 2019},
  year = {2019}
}