Publication Details
Multi-Channel Speaker Verification with Conv-Tasnet Based Beamformer
Plchot Oldřich, Ing., Ph.D. (DCGM)
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Černocký Jan, prof. Dr. Ing. (DCGM)
Conv-TasNet, beamforming, embedding extractor, speaker verification, MultiSV
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.
@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"
}