{"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/europarl-asr-a-large-corpus-of-parliamentary","title":"Europarl-ASR: A Large Corpus of Parliamentary Debates for Streaming ASR Benchmarking and Speech Data Filtering/Verbatimization","arxiv_id":null,"date":"2021-08-30","proceeding":"Interspeech 2021 8","authors":["Gonçal V. Garcés Díaz-Munío","Joan-Albert Silvestre-Cerdà","Javier Jorge","Adrià Giménez Pastor","Javier Iranzo-Sánchez","Pau Baquero-Arnal","Nahuel Roselló","Alejandro Pérez-González-de-Martos","Jorge Civera","Albert Sanchis","Alfons Juan"],"abstract":"We introduce Europarl-ASR, a large speech and text corpus of parliamentary debates including 1 300 hours of transcribed speeches and 70 million tokens of text in English extracted from European Parliament sessions. The training set is labelled with the Parliament’s non-fully-verbatim official transcripts, time-aligned. As verbatimness is critical for acoustic model training, we also provide automatically noise-filtered and automatically verbatimized transcripts of all speeches based on speech data filtering and verbatimization techniques. Additionally, 18 hours of transcribed speeches were manually verbatimized to build reliable speaker-dependent and speaker-independent development/test sets for streaming ASR benchmarking. The availability of manual non-verbatim and verbatim transcripts for dev/test speeches makes this corpus useful for the assessment of automatic filtering and verbatimization techniques. This paper describes the corpus and its creation, and provides off-line and streaming ASR baselines for both the speaker-dependent and speaker-independent tasks using the three training transcription sets. The corpus is publicly released under an open licence.","url_abs":"https://www.isca-speech.org/archive/interspeech_2021/diazmunio21_interspeech.html","url_pdf":"https://www.isca-speech.org/archive/pdfs/interspeech_2021/diazmunio21_interspeech.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":"benchmarking","task_name":"Benchmarking"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"speech-recognition","task_name":"Speech Recognition"}],"methods":[],"datasets_introduced":[{"slug":"europarl-asr","name":"Europarl-ASR","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-recognition-on-europarl-asr-en-guest","task":"Speech Recognition","dataset":"Europarl-ASR EN Guest-test","model":"mllp_2021_offline_verb","rank_in_archive_order":2,"of":3,"metrics":{"WER":"7.0"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-europarl-asr-en-guest","task":"Speech Recognition","dataset":"Europarl-ASR EN Guest-test","model":"mllp_2021_streaming_verb","rank_in_archive_order":3,"of":3,"metrics":{"WER":"7.3"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-europarl-asr-en-mep","task":"Speech Recognition","dataset":"Europarl-ASR EN MEP-test","model":"mllp_2021_offline_filt","rank_in_archive_order":1,"of":2,"metrics":{"WER":"7.8"},"uses_additional_data":false},{"leaderboard":"/sota/speech-recognition-on-europarl-asr-en-mep","task":"Speech Recognition","dataset":"Europarl-ASR EN MEP-test","model":"mllp_2021_streaming_filt","rank_in_archive_order":2,"of":2,"metrics":{"WER":"7.9"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}