Deeply Vocal Characterizer Dataset - AI & ML Training Data, South Korea

The Vocal Characterizer Dataset is a human nonverbal vocal sound dataset consisting of 56.7 hours of short clips from 1419 speakers, crowdsourced by the general public in South Korea and validated by the AI data platform. Also, the dataset includes metadata such as age, sex, noise level, and quality of utterance. 16 classes of Included human nonverbal sound contain ‘teeth-chattering’, ‘teeth-grinding’, ‘tongue-clicking’, ‘nose-blowing’, ‘coughing’, ‘yawning’, ‘throat-clearing’, ‘sighing’, ‘lip-popping’, ‘lip-smacking’, ‘panting’, ’crying’, ‘laughing’, ‘sneezing’, ‘moaning’, and ‘screaming’. The dataset is the first dataset to the world due to its large volume, various types of nonverbal vocal cues, and various participants. We expect that the utilization of this dataset would bring precise detection of the nonverbal vocal cues, and a better understanding of the human conversation. We're ready to deliver further information, statistics, or samples upon request. Don't hesitate to reach out! *The dataset can be delivered as either original wav files(44,100Hz, 16-bit PCM, 1-channel) or a single compressed h5 file(resampled to 16,000Hz).