Publication Details

Extracting speaker and emotion information from self-supervised speech models via channel-wise correlations

STAFYLAKIS, T.; MOŠNER, L.; KAKOUROS, S.; PLCHOT, O.; BURGET, L.; ČERNOCKÝ, J. Extracting speaker and emotion information from self-supervised speech models via channel-wise correlations. In 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings. Doha: IEEE Signal Processing Society, 2023. p. 1136-1143. ISBN: 978-1-6654-7189-3.
Czech title
Extrakce informací o mluvčím a emocích ze self-supervised modelů řeči pomocí korelace po kanálech
Type
conference paper
Language
English
Authors
URL
Keywords

Speaker identification, speaker verification, emotion recognition, self-supervised models

Abstract

Self-supervised learning of speech representations from large amounts of unlabeled data has enabled state-of-the-art results in several speech processing tasks. Aggregating these speech representations across time is typically approached by using descriptive statistics, and in particular, using the first- and second-order statistics of representation coefficients. In this paper, we examine an alternative way of extracting speaker and emotion information from self-supervised trained models, based on the correlations between the coefficients of the representations - correlation pooling. We show improvements over mean pooling and further gains when the pooling methods are combined via fusion. The code is available at github.com/Lamomal/s3prl_correlation.

Published
2023
Pages
1136–1143
Proceedings
2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings
Conference
Spoken Language Technology Workshop 2022, Doha, QA
ISBN
978-1-6654-7189-3
Publisher
IEEE Signal Processing Society
Place
Doha
DOI
UT WoS
000968851900153
EID Scopus
BibTeX
@inproceedings{BUT185160,
  author="STAFYLAKIS, T. and MOŠNER, L. and KAKOUROS, S. and PLCHOT, O. and BURGET, L. and ČERNOCKÝ, J.",
  title="Extracting speaker and emotion information from self-supervised speech models via channel-wise correlations",
  booktitle="2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings",
  year="2023",
  pages="1136--1143",
  publisher="IEEE Signal Processing Society",
  address="Doha",
  doi="10.1109/SLT54892.2023.10023345",
  isbn="978-1-6654-7189-3",
  url="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10023345"
}
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