{"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/fast-variational-bayes-for-heavy-tailed-plda","title":"Fast variational Bayes for heavy-tailed PLDA applied to i-vectors and x-vectors","arxiv_id":"1803.09153","date":"2018-03-24","proceeding":null,"authors":["Anna Silnova","Niko Brummer","Daniel Garcia-Romero","David Snyder","Lukas Burget"],"abstract":"The standard state-of-the-art backend for text-independent speaker\nrecognizers that use i-vectors or x-vectors, is Gaussian PLDA (G-PLDA),\nassisted by a Gaussianization step involving length normalization. G-PLDA can\nbe trained with both generative or discriminative methods. It has long been\nknown that heavy-tailed PLDA (HT-PLDA), applied without length normalization,\ngives similar accuracy, but at considerable extra computational cost. We have\nrecently introduced a fast scoring algorithm for a discriminatively trained\nHT-PLDA backend. This paper extends that work by introducing a fast,\nvariational Bayes, generative training algorithm. We compare old and new\nbackends, with and without length-normalization, with i-vectors and x-vectors,\non SRE'10, SRE'16 and SITW.","url_abs":"http://arxiv.org/abs/1803.09153v1","url_pdf":"http://arxiv.org/pdf/1803.09153v1.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":"fast-variational-bayes-for-heavy-tailed-plda","repo_url":"https://github.com/bsxfan/meta-embeddings","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}