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Large Raw Emotional Dataset with Aggregation Mechanism

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arxiv 2212.12266 v1 pith:TJG5HFCL submitted 2022-12-23 eess.AS

Large Raw Emotional Dataset with Aggregation Mechanism

Vladimir Kondratenko , Artem Sokolov , Nikolay Karpov , Oleg Kutuzov , Nikita Savushkin , Fyodor Minkin This is my paper
classification eess.AS
keywords datamodelactedaudiodushareal-lifespeechactual
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 5 Pith papers

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    OPPO applies RL with an Omni-Perception Reward and masked-input KL loss to boost cue utilization and suppress hallucinations in emotion reasoning MLLMs, claiming SOTA results on MER-UniBench, MME-Emotion, and MEP-Bench.

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  4. The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation

    eess.AS 2026-04 unverdicted novelty 6.0

    Emotion embedding similarities are unsuitable for zero-shot evaluation of emotional expressiveness in speech generation due to confounding by non-emotional acoustic features.

  5. The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation

    eess.AS 2026-04 conditional novelty 6.0

    Emotion-embedding cosine similarity (EMO-SIM) fails to track emotion under speaker or wording changes and misaligns with human perception, so it is unreliable for evaluating expressive speech generation.