Publication detail

Learnable Sparse Filterbank for Speaker Verification

PENG, J. GU, R. MOŠNER, L. PLCHOT, O. BURGET, L. ČERNOCKÝ, J.

Original Title

Learnable Sparse Filterbank for Speaker Verification

Type

conference paper

Language

English

Original Abstract

Recently, feature extraction with learnable filters was extensively investigated with speaker verification systems, with filters learned both in time- and frequency-domains. Most of the learned schemes however end up with filters close to their initialization (e.g. Mel filterbank) or filters strongly limited by their constraints. In this paper, we propose a novel learnable sparse filterbank, named LearnSF, by exclusively optimizing the sparsity of the filterbank, that does not explicitly constrain the filters to follow pre-defined distribution. After standard pre-processing (STFT and square of the magnitude spectrum), the learnable sparse filterbank is employed, with its normalized outputs fed into a neural network predicting the speaker identity. We evaluated the performance of the proposed approach on both VoxCeleb and CNCeleb datasets. The experimental results demonstrate the effectiveness of the proposed LearnSF compared to both widely-used acoustic features and existing parameterized learnable front-ends.

Keywords

learnable filter, sparse filtering, sparsity, speaker verification

Authors

PENG, J.; GU, R.; MOŠNER, L.; PLCHOT, O.; BURGET, L.; ČERNOCKÝ, J.

Released

18. 9. 2022

Publisher

International Speech Communication Association

Location

Incheon

ISBN

1990-9772

Periodical

Proceedings of Interspeech

Number

9

State

French Republic

Pages from

5110

Pages to

5114

Pages count

5

URL

https://www.isca-speech.org/archive/pdfs/interspeech_2022/peng22e_interspeech.pdf

BibTex

@inproceedings{BUT179826,
  author="PENG, J. and GU, R. and MOŠNER, L. and PLCHOT, O. and BURGET, L. and ČERNOCKÝ, J.",
  title="Learnable Sparse Filterbank for Speaker Verification",
  booktitle="Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
  year="2022",
  journal="Proceedings of Interspeech",
  number="9",
  pages="5110--5114",
  publisher="International Speech Communication Association",
  address="Incheon",
  doi="10.21437/Interspeech.2022-11309",
  issn="1990-9772",
  url="https://www.isca-speech.org/archive/pdfs/interspeech_2022/peng22e_interspeech.pdf"
}