{"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/speech-recognition-challenge-in-the-wild","title":"Speech Recognition Challenge in the Wild: Arabic MGB-3","arxiv_id":"1709.07276","date":"2017-09-21","proceeding":null,"authors":["Ahmed Ali","Stephan Vogel","Steve Renals"],"abstract":"This paper describes the Arabic MGB-3 Challenge - Arabic Speech Recognition\nin the Wild. Unlike last year's Arabic MGB-2 Challenge, for which the\nrecognition task was based on more than 1,200 hours broadcast TV news\nrecordings from Aljazeera Arabic TV programs, MGB-3 emphasises dialectal Arabic\nusing a multi-genre collection of Egyptian YouTube videos. Seven genres were\nused for the data collection: comedy, cooking, family/kids, fashion, drama,\nsports, and science (TEDx). A total of 16 hours of videos, split evenly across\nthe different genres, were divided into adaptation, development and evaluation\ndata sets. The Arabic MGB-Challenge comprised two tasks: A) Speech\ntranscription, evaluated on the MGB-3 test set, along with the 10 hour MGB-2\ntest set to report progress on the MGB-2 evaluation; B) Arabic dialect\nidentification, introduced this year in order to distinguish between four major\nArabic dialects - Egyptian, Levantine, North African, Gulf, as well as Modern\nStandard Arabic. Two hours of audio per dialect were released for development\nand a further two hours were used for evaluation. For dialect identification,\nboth lexical features and i-vector bottleneck features were shared with\nparticipants in addition to the raw audio recordings. Overall, thirteen teams\nsubmitted ten systems to the challenge. We outline the approaches adopted in\neach system, and summarise the evaluation results.","url_abs":"http://arxiv.org/abs/1709.07276v1","url_pdf":"http://arxiv.org/pdf/1709.07276v1.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":"speech-recognition-challenge-in-the-wild","repo_url":"https://github.com/qcri/dialectID","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"arabic-speech-recognition","task_name":"Arabic Speech Recognition"},{"task_slug":"dialect-identification","task_name":"Dialect Identification"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1709.07276","atlas_url":"https://app.syntology.ai/?focus=1709.07276","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}