# BUT System Description to VoxCeleb Speaker Recognition Challenge 2019

arXiv: https://arxiv.org/abs/1910.12592 · HTML: https://mlsanity.com/paper/1910.12592/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 30 of 54)

- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [A Multi-View Approach To Audio-Visual Speaker Verification](https://mlsanity.com/paper/2102.06291/) (2021)
- [Adversarial defense for automatic speaker verification by cascaded self-supervised learning models](https://mlsanity.com/paper/2102.07047/) (2021)
- [Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning](https://mlsanity.com/paper/2106.00273/) (2021)
- [Adversarially learning disentangled speech representations for robust multi-factor voice conversion](https://mlsanity.com/paper/2102.00184/) (2021)
- [Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation](https://mlsanity.com/paper/2108.02510/) (2021)
- [VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge](https://mlsanity.com/paper/2302.10248/) (2023)
- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models](https://mlsanity.com/paper/2202.08974/) (2022)
- [Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization](https://mlsanity.com/paper/2302.12716/) (2023)
- [STC speaker recognition systems for the NIST SRE 2021](https://mlsanity.com/paper/2111.02298/) (2021)
- [Combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection](https://mlsanity.com/paper/2210.17222/) (2022)
- [Robust Speaker Recognition with Transformers Using wav2vec 2.0](https://mlsanity.com/paper/2203.15095/) (2022)
- [DSE-TTS: Dual Speaker Embedding for Cross-Lingual Text-to-Speech](https://mlsanity.com/paper/2306.14145/) (2023)
- [Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation](https://mlsanity.com/paper/2210.12635/) (2022)
- [Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Automatic Speaker Verification](https://mlsanity.com/paper/2309.14838/) (2023)
- [Real Additive Margin Softmax for Speaker Verification](https://mlsanity.com/paper/2110.09116/) (2021)
- [Royalflush Speaker Diarization System for ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge](https://mlsanity.com/paper/2202.04814/) (2022)
- [Magnitude-aware Probabilistic Speaker Embeddings](https://mlsanity.com/paper/2202.13826/) (2022)
- [Improving Transformer-based Networks With Locality For Automatic Speaker Verification](https://mlsanity.com/paper/2302.08639/) (2023)
- [Efficient Adapter Tuning of Pre-trained Speech Models for Automatic Speaker Verification](https://mlsanity.com/paper/2403.00293/) (2024)
- [MACCIF-TDNN: Multi aspect aggregation of channel and context interdependence features in TDNN-based speaker verification](https://mlsanity.com/paper/2107.03104/) (2021)
- [Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddings](https://mlsanity.com/paper/2203.14893/) (2022)
- [The Impact of Automatic Speech Transcription on Speaker Attribution](https://mlsanity.com/paper/2507.08660/) (2025)
- [A stylometric analysis of speaker attribution from speech transcripts](https://mlsanity.com/paper/2512.13667/) (2025)
- [Inclusive Speaker Verification with Adaptive thresholding](https://mlsanity.com/paper/2111.05501/) (2021)
- [Optimizing Tandem Speaker Verification and Anti-Spoofing Systems](https://mlsanity.com/paper/2201.09709/) (2022)
- [Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion](https://mlsanity.com/paper/2206.09131/) (2022)
- [Universal speaker recognition encoders for different speech segments duration](https://mlsanity.com/paper/2210.16231/) (2022)
- [Label-free Knowledge Distillation with Contrastive Loss for Light-weight Speaker Recognition](https://mlsanity.com/paper/2212.03090/) (2022)

## Abstract (arXiv)

In this report, we describe the submission of Brno University of Technology (BUT) team to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2019. We also provide a brief analysis of different systems on VoxCeleb-1 test sets. Submitted systems for both Fixed and Open conditions are a fusion of 4 Convolutional Neural Network (CNN) topologies. The first and second networks have ResNet34 topology and use two-dimensional CNNs. The last two networks are one-dimensional CNN and are based on the x-vector extraction topology. Some of the networks are fine-tuned using additive margin angular softmax. Kaldi FBanks and Kaldi PLPs were used as features. The difference between Fixed and Open systems lies in the used training data and fusion strategy. The best systems for Fixed and Open conditions achieved 1.42% and 1.26% ERR on the challenge evaluation set respectively.