---
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