Prototypical networks for small footprint text-independent speaker verification

Tom Ko, Yangbin Chen, Qing Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

18 Citations (Scopus)

Abstract

Speaker verification aims to recognize target speakers with very few enrollment utterances. Conventional approaches learn a representation model to extract the speaker embeddings for verification. Recently, there are several new approaches in meta-learning which try to learn a shared metric space. Among these approaches, prototypical networks aim at learning a non-linear mapping from the input space to an embedding space with a predefined distance metric. In this paper, we investigate the use of prototypical networks in a small footprint text-independent speaker verification task. Our work is evaluated on SRE10 evaluation set. Experiments show that prototypical networks outperform the conventional method when the amount of data per training speaker is limited.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6804-6808
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • Meta learning
  • Prototypical networks
  • Speaker verification

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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