Multi-Channel Speaker Verification with Conv-Tasnet Based Beamformer

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU144909" target="_blank" >RIV/00216305:26230/22:PU144909 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9747771" target="_blank" >https://ieeexplore.ieee.org/document/9747771</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP43922.2022.9747771" target="_blank" >10.1109/ICASSP43922.2022.9747771</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Channel Speaker Verification with Conv-Tasnet Based Beamformer

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multi-Channel Speaker Verification with Conv-Tasnet Based Beamformer

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

978-1-6654-0540-9

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

7982-7986

Název nakladatele

IEEE Signal Processing Society

Místo vydání

Singapore

Místo konání akce

Singapore

Datum konání akce

22. 5. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000864187908058