Detail publikace

Multisv: Dataset for Far-Field Multi-Channel Speaker Verification

MOŠNER, L. PLCHOT, O. BURGET, L. ČERNOCKÝ, J.

Originální název

Multisv: Dataset for Far-Field Multi-Channel Speaker Verification

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Motivated by unconsolidated data situation and the lack of a standard benchmark in the field, we complement our previous efforts and present a comprehensive corpus designed for training and evaluating text-independent multi-channel speaker verification systems. It can be readily used also for experiments with dereverberation, denoising, and speech enhancement. We tackled the ever-present problem of the lack of multi-channel training data by utilizing data simulation on top of clean parts of the Voxceleb corpus. The development and evaluation trials are based on a retransmitted Voices Obscured in Complex Environmental Settings (VOiCES) corpus, which we modified to provide multi-channel trials. We publish full recipes that create the dataset from public sources as the MultiSV dataset, and we provide results with two of our multi-channel speaker verification systems with neural network-based beamforming based either on predicting ideal binary masks or the more recent Conv-TasNet.

Klíčová slova

Multi-channel, speaker verification, MultiSV, dataset, beamforming

Autoři

MOŠNER, L.; PLCHOT, O.; BURGET, L.; ČERNOCKÝ, J.

Vydáno

27. 5. 2022

Nakladatel

IEEE Signal Processing Society

Místo

Singapore

ISBN

978-1-6654-0540-9

Kniha

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Strany od

7977

Strany do

7981

Strany počet

5

URL

https://ieeexplore.ieee.org/document/9746833

BibTex

@inproceedings{BUT178380,
  author="Ladislav {Mošner} and Oldřich {Plchot} and Lukáš {Burget} and Jan {Černocký}",
  title="Multisv: Dataset for Far-Field Multi-Channel Speaker Verification",
  booktitle="ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
  year="2022",
  pages="7977--7981",
  publisher="IEEE Signal Processing Society",
  address="Singapore",
  doi="10.1109/ICASSP43922.2022.9746833",
  isbn="978-1-6654-0540-9",
  url="https://ieeexplore.ieee.org/document/9746833"
}