Robust speaker verification using Population-based Data Augmentation

Weiwei Lin, Man Wai Mak

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

Abstract

Speaker recognition under environments with a low signal-to-noise ratio (SNR) and high reverberation level has always been challenging. Data augmentation can be applied to simulate the adverse environments that a speaker recognition system may encounter. Typically, the augmentation parameters are manually set. Recently, automatic hyper-parameter optimization using population-based learning has shown promising results. This paper proposes a population-based searching strategy for optimizing the augmentation parameters. We refer to the resulting augmentation as population-based augmentation (PBA). Instead of finding a fixed set of hyper-parameters, PBA learns a scheduler for setting the hyper-parameters. This strategy offers a considerable computation advantage over the grid search. We obtained high-performance augmentation policies using a population of six networks only. With PBA, we achieved an EER of 3.98% on the VOiCES19 evaluation set.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
Place of PublicationUSA
PublisherIEEE
Pages7642-7646
Number of pages5
ISBN (Electronic)9781665405409
DOIs
Publication statusPublished - May 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May
ISSN (Print)1520-6149

Keywords

  • Robust speaker verification
  • data augmentation
  • hyper-parameter optimization
  • population-based learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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