CL-UZH submission to the NIST SRE 2024 Speaker Recognition Evaluation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914078726815744 |
|---|---|
| author | Farhadipour, Aref Liu, Shiran Chapariniya, Masoumeh Vyshnevetska, Valeriia Madikeri, Srikanth Vukovic, Teodora Dellwo, Volker |
| author_facet | Farhadipour, Aref Liu, Shiran Chapariniya, Masoumeh Vyshnevetska, Valeriia Madikeri, Srikanth Vukovic, Teodora Dellwo, Volker |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00952 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CL-UZH submission to the NIST SRE 2024 Speaker Recognition Evaluation Farhadipour, Aref Liu, Shiran Chapariniya, Masoumeh Vyshnevetska, Valeriia Madikeri, Srikanth Vukovic, Teodora Dellwo, Volker Audio and Speech Processing Sound 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. |
| title | CL-UZH submission to the NIST SRE 2024 Speaker Recognition Evaluation |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2510.00952 |