Detail publikace

Multi-Channel Speaker Verification with Conv-Tasnet Based Beamformer

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

Originální název

Multi-Channel Speaker Verification with Conv-Tasnet Based Beamformer

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

We focus on the problem of speaker recognition in far-field multichannel data. The main contribution is introducing an alternative way of predicting spatial covariance matrices (SCMs) for a beamformer from the time domain signal. We propose to use ConvTasNet, a well-known source separation model, and we adapt it to perform speech enhancement by forcing it to separate speech and additive noise. We experiment with using the STFT of Conv-TasNet outputs to obtain SCMs of speech and noise, and finally, we fine-tune this multi-channel frontend w.r.t. speaker verification objective. We successfully tackle the problem of the lack of a realistic multichannel training set by using simulated data of MultiSV corpus. The analysis is performed on its retransmitted and simulated test parts. We achieve consistent improvements with a 2.7 times smaller model than the baseline based on a scheme with mask estimating NN.

Klíčová slova

Conv-TasNet, beamforming, embedding extractor, speaker verification, MultiSV

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

7982

Strany do

7986

Strany počet

5

URL

https://ieeexplore.ieee.org/document/9747771

BibTex

@inproceedings{BUT178381,
  author="Ladislav {Mošner} and Oldřich {Plchot} and Lukáš {Burget} and Jan {Černocký}",
  title="Multi-Channel Speaker Verification with Conv-Tasnet Based Beamformer",
  booktitle="ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
  year="2022",
  pages="7982--7986",
  publisher="IEEE Signal Processing Society",
  address="Singapore",
  doi="10.1109/ICASSP43922.2022.9747771",
  isbn="978-1-6654-0540-9",
  url="https://ieeexplore.ieee.org/document/9747771"
}