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EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies | | Fred W. Buhl
; | | Date: |
2 Jan 2023 | | Abstract: | 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 32,040 samples from 365 speakers, 16.91 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 this https URL. | | Source: | arXiv, 2301.00508 | | Services: | Forum | Review | PDF | Favorites | |
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