  Vocal Bursts -- short, non-speech vocalizations that convey emotions, such as
laughter, cries, sighs, moans, and groans -- are an often-overlooked aspect of
speech emotion recognition, but an important aspect of human vocal
communication. One barrier to study of these interesting vocalizations is a
lack of large datasets. I am pleased to introduce the EmoGator dataset, which
consists of 32,130 samples from 357 speakers, 16.9654 hours of audio; each
sample classified into one of 30 distinct emotion categories by the speaker.
Several different approaches to construct classifiers to identify emotion
categories will be discussed, and directions for future research will be
suggested. Data set is available for download from
https://github.com/fredbuhl/EmoGator.
