PROTOTYPICAL NETWORKS FOR SMALL FOOTPRINT TEXT-INDEPENDENT SPEAKER VERIFICATION

Tom Ko, Yangbin Chen*, Qing Li

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

19 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
Subtitle of host publicationProceedings
PublisherIEEE
Pages6804-6808
Volume2020-May
ISBN (Electronic)978-1-5090-6631-5
ISBN (Print)978-1-5090-6632-2
DOIs
Publication statusPublished - May 2020
Event45th International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - virtual
Duration: 4 May 20208 May 2020
https://2020.ieeeicassp.org/

Publication series

NameInternational Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PublisherIEEE
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

Conference45th International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Period4/05/208/05/20
Internet address

Research Keywords

  • Meta learning
  • Prototypical networks
  • Speaker verification

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