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).

搜集汇总

背景与挑战

背景概述

该数据集是一个来自韩国公众众包的非语言人声数据集，包含1419名说话者的56.7小时音频片段，涵盖16类声音如咳嗽、笑声和尖叫等。作为首个大规模、多类型的非语言人声数据集，它旨在提升非语言线索的检测精度和对人类对话的理解，并提供wav或h5格式文件。