{"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/a-textless-metric-for-speech-to-speech","title":"A Textless Metric for Speech-to-Speech Comparison","arxiv_id":"2210.11835","date":"2022-10-21","proceeding":null,"authors":["Laurent Besacier","Swen Ribeiro","Olivier Galibert","Ioan Calapodescu"],"abstract":"In this paper, we introduce a new and simple method for comparing speech utterances without relying on text transcripts. Our speech-to-speech comparison metric utilizes state-of-the-art speech2unit encoders like HuBERT to convert speech utterances into discrete acoustic units. We then propose a simple and easily replicable neural architecture that learns a speech-based metric that closely corresponds to its text-based counterpart. This textless metric has numerous potential applications, including evaluating speech-to-speech translation for oral languages, languages without dependable ASR systems, or to avoid the need for ASR transcription altogether. This paper also shows that for speech-to-speech translation evaluation, ASR-BLEU (which consists in automatically transcribing both speech hypothesis and reference and compute sentence-level BLEU between transcripts) is a poor proxy to real text-BLEU even when ASR system is strong.","url_abs":"https://arxiv.org/abs/2210.11835v2","url_pdf":"https://arxiv.org/pdf/2210.11835v2.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":"a-textless-metric-for-speech-to-speech","repo_url":"https://github.com/besacier/textless-metric","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"speech-to-speech-translation","task_name":"Speech-to-Speech Translation"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2210.11835","atlas_url":"https://app.syntology.ai/?focus=2210.11835","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}