Detail výsledku

Learnable Sparse Filterbank for Speaker Verification

PENG, J.; GU, R.; MOŠNER, L.; PLCHOT, O.; BURGET, L.; ČERNOCKÝ, J. Learnable Sparse Filterbank for Speaker Verification. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. Proceedings of Interspeech. Incheon: International Speech Communication Association, 2022. no. 9, p. 5110-5114. ISSN: 1990-9772.
Typ
článek ve sborníku konference
Jazyk
anglicky
Autoři
Peng Junyi, UPGM (FIT)
GU, R.
Mošner Ladislav, Ing., UPGM (FIT)
Plchot Oldřich, Ing., Ph.D., UPGM (FIT)
Burget Lukáš, doc. Ing., Ph.D., UPGM (FIT)
Černocký Jan, prof. Dr. Ing., UPGM (FIT)
Abstrakt

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.

Klíčová slova

learnable filter, sparse filtering, sparsity, speaker verification

URL
Rok
2022
Strany
5110–5114
Časopis
Proceedings of Interspeech, č. 9, ISSN 1990-9772
Sborník
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Konference
Interspeech Conference
Vydavatel
International Speech Communication Association
Místo
Incheon
DOI
10.21437/Interspeech.2022-11309
UT WoS
000900724505058
EID Scopus
2-s2.0-85140077879
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"
}
Soubory
Projekty
Multi-lingualita v řečových technologiích, MŠMT, INTER-EXCELLENCE - Podprogram INTER-ACTION, LTAIN19087, zahájení: 2020-01-01, ukončení: 2023-08-31, ukončen
Neuronové reprezentace v multimodálním a mnohojazyčném modelování, GAČR, Grantové projekty exelence v základním výzkumu EXPRO - 2019, GX19-26934X, zahájení: 2019-01-01, ukončení: 2023-12-31, ukončen
Výměny pro výzkum řeči a technologií, EU, Horizon 2020, zahájení: 2021-01-01, ukončení: 2025-12-31, řešení
Výzkumné skupiny
Výzkumná skupina dolování dat z řeči BUT Speech@FIT (VZ SPEECH)
Pracoviště
Ústav počítačové grafiky a multimédií (UPGM)
Nahoru