# CL-UZH submission to the NIST SRE 2024 Speaker Recognition Evaluation

arXiv: https://arxiv.org/abs/2510.00952 · HTML: https://mlsanity.com/paper/2510.00952/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 6 of 6)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark](https://mlsanity.com/paper/2407.11510/) (2024)
- [Gammatonegram Representation for End-to-End Dysarthric Speech Processing Tasks: Speech Recognition, Speaker Identification, and Intelligibility Assessment](https://mlsanity.com/paper/2307.03296/) (2023)
- [Quantifying and Reducing Speaker Heterogeneity within the Common Voice Corpus for Phonetic Analysis](https://mlsanity.com/paper/2506.00733/) (2025)
- [Comparative Analysis of Modality Fusion Approaches for Audio-Visual Person Identification and Verification](https://mlsanity.com/paper/2409.00562/) (2024)

## Built on by (most cited first, 1 of 1)

- [Spoofing-Aware Speaker Verification via Wavelet Prompt Tuning and Multi-Model Ensembles](https://mlsanity.com/paper/2601.17557/) (2026)

## Abstract (arXiv)

The CL-UZH team submitted one system each for the fixed and open conditions of the NIST SRE 2024 challenge. For the closed-set condition, results for the audio-only trials were achieved using the X-vector system developed with Kaldi. For the audio-visual results we used only models developed for the visual modality. Two sets of results were submitted for the open-set and closed-set conditions, one based on a pretrained model using the VoxBlink2 and VoxCeleb2 datasets. An Xvector-based model was trained from scratch using the CTS superset dataset for the closed set. In addition to the submission of the results of the SRE24 evaluation to the competition website, we talked about the performance of the proposed systems on the SRE24 evaluation in this report.