Recognizing human non-speech vocalizations is an important task and has broad applications such as automatic sound transcription and health condition monitoring. However, existing datasets have a relatively small number of vocal sound samples or noisy labels. As a consequence, state-of-the-art audio event classification models may not perform well in detecting human vocal sounds. To support research on building robust and accurate vocal sound recognition, we have created a VocalSound dataset consisting of over 21,000 crowdsourced recordings of laughter, sighs, coughs, throat clearing, sneezes, and sniffs from 3,365 unique subjects. Experiments show that the vocal sound recognition performance of a model can be significantly improved by 41.9% by adding VocalSound dataset to an existing dataset as training material. In addition, different from previous datasets, the VocalSound dataset contains meta information such as speaker age, gender, native language, country, and health condition.

翻译：承认人类非声音是一项重要任务,具有诸如自动声响转录和健康状况监测等广泛应用。然而,现有数据集拥有相对较少的声响样本或噪音标签。因此,最先进的音频事件分类模型在探测人类声音方面可能表现不佳。为了支持建立有力和准确声音识别的研究,我们创建了VocalSound数据集,由21,000多份来自3,365个独特主题的众传录音、叹息、咳嗽、清喉、喷嚏和嗅闻组成。实验显示,通过将VocalSound数据集作为培训材料加入现有数据集,可以大大改进模型声音识别性能的41.9%。此外,VocalSound数据集与以前的数据集不同,包含语音年龄、性别、母语、国家和健康状况等元信息。