speech-utterances

VocalSound is a crowdsourced dataset of human non-speech vocal sounds for audio classification tasks. It was created to support research on building robust and accurate vocal sound recognition systems, addressing limitations in existing datasets that have relatively small numbers of samples or noisy labels.

Source: https://github.com/YuanGongND/vocalsound

Paper: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing

License: Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)

Dataset Statistics

Metric	Value
Total Samples	21,024
Unique Speakers	3,365
Countries Represented	60
Classes	6

Data Splits

Split	Samples	Percentage
Training	15,570	74%
Validation	1,860	9%
Evaluation	3,594	17%

All three sets are speaker-independent. The evaluation set has been manually verified for quality.

Classes (Balanced)

Each class contains 3,504 samples:

Laughter
Sigh
Cough
Throat clearing
Sneeze
Sniff

Speaker Demographics

Gender

Male: 55%
Female: 45%

Age Distribution

Range: 18-80 years
Majority: 20-40 years
Subjects over 50: 321

Geographic Distribution

United States: 60.3%
India: 10.8%
Brazil: 8.3%
Other countries: 20.6%

Native Languages

English: 67.2%
Portuguese: 8.7%
Italian: 6.8%
Other languages: 17.3%

Health Status

4% of subjects reported symptoms affecting speech

Audio Specifications

Original Format

Sample Rate: 44.1 kHz
Format: WAV
Mean Duration: 4.18 seconds
Median Duration: 3.72 seconds
Standard Deviation: 1.81 seconds

WebDataset Format (This Collection)

Sample Rate: 48 kHz
Bit Depth: 16-bit
Channels: Mono
Format: FLAC
Total Tar Files: 22

Baseline Performance

Using an EfficientNet-B0 based classifier:

Overall Accuracy: 90.5%
Male Speakers: 89.2%
Female Speakers: 91.9%

Adding VocalSound to existing training data improves vocal sound recognition performance by 41.9%.

Authors

Yuan Gong (MIT CSAIL)
Jin Yu (Signify)
James Glass (MIT CSAIL)

Citation

@INPROCEEDINGS{gong_vocalsound,
  author={Gong, Yuan and Yu, Jin and Glass, James},
  booktitle={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  title={Vocalsound: A Dataset for Improving Human Vocal Sounds Recognition},
  year={2022},
  pages={151-155},
  doi={10.1109/ICASSP43922.2022.9746828}
}

References

GitHub Repository: https://github.com/YuanGongND/vocalsound
arXiv Paper: https://arxiv.org/abs/2205.03433
IEEE Xplore: https://ieeexplore.ieee.org/document/9746828