{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fine-tuning-wav2vec2-for-speaker-recognition","title":"Fine-tuning wav2vec2 for speaker recognition","arxiv_id":"2109.15053","date":"2021-09-30","proceeding":null,"authors":["Nik Vaessen","David A. van Leeuwen"],"abstract":"This paper explores applying the wav2vec2 framework to speaker recognition instead of speech recognition. We study the effectiveness of the pre-trained weights on the speaker recognition task, and how to pool the wav2vec2 output sequence into a fixed-length speaker embedding. To adapt the framework to speaker recognition, we propose a single-utterance classification variant with CE or AAM softmax loss, and an utterance-pair classification variant with BCE loss. Our best performing variant, w2v2-aam, achieves a 1.88% EER on the extended voxceleb1 test set compared to 1.69% EER with an ECAPA-TDNN baseline. Code is available at https://github.com/nikvaessen/w2v2-speaker.","url_abs":"https://arxiv.org/abs/2109.15053v2","url_pdf":"https://arxiv.org/pdf/2109.15053v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fine-tuning-wav2vec2-for-speaker-recognition","repo_url":"https://github.com/nikvaessen/w2v2-speaker","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fine-tuning-wav2vec2-for-speaker-recognition","repo_url":"https://github.com/MS-P3/code7/tree/main/wav2vec2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"fine-tuning-wav2vec2-for-speaker-recognition","repo_url":"https://github.com/MindCode-4/code-5/tree/main/wav2vec2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"fine-tuning-wav2vec2-for-speaker-recognition","repo_url":"https://github.com/pwc-1/Paper-9/tree/main/1/wav2vec2_with_lm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"speaker-recognition","task_name":"Speaker Recognition"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speaker-recognition-on-voxceleb1","task":"Speaker Recognition","dataset":"VoxCeleb1","model":"w2v2-aam","rank_in_archive_order":2,"of":2,"metrics":{"EER":"1.88"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2109.15053","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}