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

EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies