The Annotated In-The-Wild (AITW) dataset accompanies the paper “Whilter: A Whisper-based Data Filter for ‘in-the-wild’ Speech Corpora Using Utterance-level Multi-Task Classification”, accepted at Interspeech 2025 in Rotterdam. This dataset supports research into automated filtering of noisy or undesirable audio segments in large-scale, real-world speech corpora, particularly for training high-quality English TTS and ASR models.
\nAITW includes over 21,000 manually labeled audio samples (≈64 hours) from two popular in-the-wild speech datasets (Emilia and YODAS). Each audio clip is annotated at the utterance level with binary or numerical labels for five key properties.
\nNumerical labels:
\nBinary labels:
\nAnnotations were performed by expert annotators using a custom Label Studio interface, with consistent guidelines applied across all tasks. This dataset enables the benchmarking of multi-task classification models like Whilter and comparison with single-task baselines.
\nAITW is designed to foster further research in scalable speech data curation and low-resource dataset bootstrapping. We encourage contributions and improvements through the included Label Studio GUI.
\nFiles include:
\nIf you use this dataset, please cite:
\n\n\nW. Ravenscroft, G. Close, K. Bower-Morris, J. Stacey, D. Sityaev, K. Hong. “Whilter: A Whisper-based Data Filter for ‘in-the-wild’ Speech Corpora Using Utterance-level Multi-Task Classification,” Interspeech 2025.
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License:
\nCreative Commons Attribution 4.0 International (CC BY 4.0)
\n", "languages": [ { "id": "eng", "title": { "en": "English" } }, { "id": "mul", "title": { "en": "Multiple languages" } } ], "publication_date": "2025-08-17", "publisher": "Zenodo", "references": [ { "reference": "X. Li, S. Takamichi, T. Saeki, W. Chen, S. Shiota and S. Watanabe, \"Yodas: Youtube-Oriented Dataset for Audio and Speech,\" 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Taipei, Taiwan, 2023, pp. 1-8, doi: 10.1109/ASRU57964.2023.10389689. keywords: {Training;Video on demand;Conferences;Pipelines;Self-supervised learning;Manuals;Data collection;multilingual speech processing;speech recognition;large-scale speech dataset}," }, { "reference": "H. He et al., \"Emilia: An Extensive, Multilingual, and Diverse Speech Dataset For Large-Scale Speech Generation,\" 2024 IEEE Spoken Language Technology Workshop (SLT), Macao, 2024, pp. 885-890, doi: 10.1109/SLT61566.2024.10832365. keywords: {Training;Technological innovation;Annotations;Conferences;Pipelines;Training data;Transforms;Speech;Data models;Multilingual;Extensive Multilingual and Diverse Dataset;Large-scale Speech Generation}," } ], "resource_type": { "id": "dataset", "title": { "de": "Datensatz", "en": "Dataset" } }, "rights": [ { "description": { "en": "The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited." }, "icon": "cc-by-icon", "id": "cc-by-4.0", "props": { "scheme": "spdx", "url": "https://creativecommons.org/licenses/by/4.0/legalcode" }, "title": { "en": "Creative Commons Attribution 4.0 International" } } ], "subjects": [ { "subject": "Audio Classification" }, { "subject": "In-The-Wild Data" }, { "subject": "Speech Datasets" } ], "title": "AITW: The Annotated In-the-Wild Dataset for Filtering of In-the-Wild Speech Data", "version": "1.1" }, "parent": { "access": { "owned_by": { "user": "1330242" }, "settings": { "accept_conditions_text": null, "allow_guest_requests": false, "allow_user_requests": false, "secret_link_expiration": 0 } }, "communities": {}, "id": "15534661", "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.15534661", "provider": "datacite" } } }, "pids": { "doi": { "client": "datacite", "identifier": "10.5281/zenodo.16937418", "provider": "datacite" }, "oai": { "identifier": "oai:zenodo.org:16937418", "provider": "oai" } }, "revision_id": 4, "stats": { "all_versions": { "data_volume": 379812275.0, "downloads": 315, "unique_downloads": 266, "unique_views": 217, "views": 258 }, "this_version": { "data_volume": 135972517.0, "downloads": 139, "unique_downloads": 122, "unique_views": 67, "views": 82 } }, "status": "published", "swh": {}, "updated": "2025-08-26T08:01:20.952435+00:00", "versions": { "index": 2, "is_latest": true } }