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---
dataset_info:
features:
- name: audio
dtype: string
- name: Crowd_Worker_1
dtype: string
- name: Crowd_Worker_2
dtype: string
- name: Crowd_Worker_3
dtype: string
- name: Expert_1
dtype: string
- name: Expert_2
dtype: string
- name: Expert_3
dtype: string
- name: source_dataset
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 475376
num_examples: 500
download_size: 112382
dataset_size: 475376
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
language:
- en
---
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 |