{"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/large-raw-emotional-dataset-with-aggregation","title":"Large Raw Emotional Dataset with Aggregation Mechanism","arxiv_id":"2212.12266","date":"2022-12-23","proceeding":null,"authors":["Vladimir Kondratenko","Artem Sokolov","Nikolay Karpov","Oleg Kutuzov","Nikita Savushkin","Fyodor Minkin"],"abstract":"We present a new data set for speech emotion recognition (SER) tasks called Dusha. The corpus contains approximately 350 hours of data, more than 300 000 audio recordings with Russian speech and their transcripts. Therefore it is the biggest open bi-modal data collection for SER task nowadays. It is annotated using a crowd-sourcing platform and includes two subsets: acted and real-life. Acted subset has a more balanced class distribution than the unbalanced real-life part consisting of audio podcasts. So the first one is suitable for model pre-training, and the second is elaborated for fine-tuning purposes, model approbation, and validation. This paper describes pre-processing routine, annotation, and experiment with a baseline model to demonstrate some actual metrics which could be obtained with the Dusha data set.","url_abs":"https://arxiv.org/abs/2212.12266v1","url_pdf":"https://arxiv.org/pdf/2212.12266v1.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":"large-raw-emotional-dataset-with-aggregation","repo_url":"https://github.com/salute-developers/golos","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"speech-emotion-recognition","task_name":"Speech Emotion Recognition"}],"methods":[],"datasets_introduced":[{"slug":"dusha","name":"Dusha","full_name":"Dusha Crowd, Dusha Podcast"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-emotion-recognition-on-dusha-crowd","task":"Speech Emotion Recognition","dataset":"Dusha Crowd","model":"Dusha baseline","rank_in_archive_order":1,"of":1,"metrics":{"Macro F1":"0.77","UA":"0.83","WA":"0.76"},"uses_additional_data":false},{"leaderboard":"/sota/speech-emotion-recognition-on-dusha-podcast","task":"Speech Emotion Recognition","dataset":"Dusha Podcast","model":"Dusha baseline","rank_in_archive_order":1,"of":1,"metrics":{"Macro F1":"0.54","UA":"0.89","WA":"0.53"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2212.12266","atlas_url":"https://app.syntology.ai/?focus=2212.12266","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.12266"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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