{"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/the-fifth-chime-speech-separation-and","title":"The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines","arxiv_id":"1803.10609","date":"2018-03-28","proceeding":null,"authors":["Jon Barker","Shinji Watanabe","Emmanuel Vincent","Jan Trmal"],"abstract":"The CHiME challenge series aims to advance robust automatic speech\nrecognition (ASR) technology by promoting research at the interface of speech\nand language processing, signal processing , and machine learning. This paper\nintroduces the 5th CHiME Challenge, which considers the task of distant\nmulti-microphone conversational ASR in real home environments. Speech material\nwas elicited using a dinner party scenario with efforts taken to capture data\nthat is representative of natural conversational speech and recorded by 6\nKinect microphone arrays and 4 binaural microphone pairs. The challenge\nfeatures a single-array track and a multiple-array track and, for each track,\ndistinct rankings will be produced for systems focusing on robustness with\nrespect to distant-microphone capture vs. systems attempting to address all\naspects of the task including conversational language modeling. We discuss the\nrationale for the challenge and provide a detailed description of the data\ncollection procedure, the task, and the baseline systems for array\nsynchronization, speech enhancement, and conventional and end-to-end ASR.","url_abs":"http://arxiv.org/abs/1803.10609v1","url_pdf":"http://arxiv.org/pdf/1803.10609v1.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":[],"tasks":[{"task_slug":"automatic-speech-recognition-2","task_name":"Automatic Speech Recognition"},{"task_slug":"automatic-speech-recognition","task_name":"Automatic Speech Recognition (ASR)"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"speech-enhancement","task_name":"Speech Enhancement"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"},{"task_slug":"speech-separation","task_name":"Speech Separation"},{"task_slug":"speech-recognition-1","task_name":"speech-recognition"}],"methods":[],"datasets_introduced":[{"slug":"chime-5","name":"CHiME-5","full_name":"CHiME Speech Separation and Recognition Challenge"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1803.10609","atlas_url":"https://app.syntology.ai/?focus=1803.10609","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}