{"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/automatic-quality-assessment-for-speech","title":"Automatic Quality Assessment for Speech Translation Using Joint ASR and MT Features","arxiv_id":"1609.06049","date":"2016-09-20","proceeding":null,"authors":["Ngoc-Tien Le","Benjamin Lecouteux","Laurent Besacier"],"abstract":"This paper addresses automatic quality assessment of spoken language\ntranslation (SLT). This relatively new task is defined and formalized as a\nsequence labeling problem where each word in the SLT hypothesis is tagged as\ngood or bad according to a large feature set. We propose several word\nconfidence estimators (WCE) based on our automatic evaluation of transcription\n(ASR) quality, translation (MT) quality, or both (combined ASR+MT). This\nresearch work is possible because we built a specific corpus which contains\n6.7k utterances for which a quintuplet containing: ASR output, verbatim\ntranscript, text translation, speech translation and post-edition of\ntranslation is built. The conclusion of our multiple experiments using joint\nASR and MT features for WCE is that MT features remain the most influent while\nASR feature can bring interesting complementary information. Our robust quality\nestimators for SLT can be used for re-scoring speech translation graphs or for\nproviding feedback to the user in interactive speech translation or\ncomputer-assisted speech-to-text scenarios.","url_abs":"http://arxiv.org/abs/1609.06049v1","url_pdf":"http://arxiv.org/pdf/1609.06049v1.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":"automatic-quality-assessment-for-speech","repo_url":"https://github.com/besacier/WCE-LIG","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"speech-to-text","task_name":"Speech-to-Text"},{"task_slug":"translation","task_name":"Translation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}