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CLESC-dataset (Crowd Labeled Emotions and Speech Characteristics) is a dataset of 500 audio samples with transcriptions mixed of 2 open sourced Common Voice (100) and Voxceleb* (400) with voice features labels. We focus on annotating scalable voice characteristics such as pace (slow, normal, fast, variable), pitch (low, medium, high, variable), and volume (quiet, medium, loud, variable) as well as labeling emotions and unique voice features (free input, based on instructions provided).
Curated by: Evgeniya Sukhodolskaya, Ilya Kochik (Toloka)
[1] J. S. Chung, A. Nagrani, A. Zisserman VoxCeleb2: Deep Speaker Recognition INTERSPEECH, 2018.
[2] A. Nagrani, J. S. Chung, A. Zisserman VoxCeleb: a large-scale speaker identification dataset INTERSPEECH, 2017
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