  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.
