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

arXiv: https://arxiv.org/abs/2111.06458 · HTML: https://mlsanity.com/paper/2111.06458/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines](https://mlsanity.com/paper/1803.10609/) (2018)
- [Voices Obscured in Complex Environmental Settings (VOICES) corpus](https://mlsanity.com/paper/1804.05053/) (2018)
- [VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2012.06867/) (2020)
- [The INTERSPEECH 2020 Far-Field Speaker Verification Challenge](https://mlsanity.com/paper/2005.08046/) (2020)
- [The VOiCES from a Distance Challenge 2019 Evaluation Plan](https://mlsanity.com/paper/1902.10828/) (2019)
- [HI-MIA : A Far-field Text-Dependent Speaker Verification Database and the Baselines](https://mlsanity.com/paper/1912.01231/) (2019)
- [Libri-adhoc40: A dataset collected from synchronized ad-hoc microphone arrays](https://mlsanity.com/paper/2103.15118/) (2021)
- [Attention-based multi-channel speaker verification with ad-hoc microphone arrays](https://mlsanity.com/paper/2107.00178/) (2021)

## Built on by (most cited first, 3 of 3)

- [SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions](https://mlsanity.com/paper/2509.17091/) (2025)
- [Self-supervised learning with diffusion-based multichannel speech enhancement for speaker verification under noisy conditions](https://mlsanity.com/paper/2307.02244/) (2023)
- [LC4SV: A Denoising Framework Learning to Compensate for Unseen Speaker Verification Models](https://mlsanity.com/paper/2311.16604/) (2023)

## Abstract (arXiv)

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.