Related papers: MultiSV: Dataset for Far-Field Multi-Channel Speaker Verification

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

URL: http://arxiv.org/abs/2111.06458v1
Date: Thu, 11 Nov 2021 20:55:58 GMT
Title: MultiSV: Dataset for Far-Field Multi-Channel Speaker Verification
Authors: Ladislav Mo\v{s}ner, Old\v{r}ich Plchot, Luk\'a\v{s} Burget, Jan \v{C}ernock\'y
Abstract summary: We 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.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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 dataset. 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 corpus, 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.

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