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

arXiv: https://arxiv.org/abs/2301.00508 · HTML: https://mlsanity.com/paper/2301.00508/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 2 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Very Deep Convolutional Neural Networks for Raw Waveforms](https://mlsanity.com/paper/1610.00087/) (2016)
- [The ICML 2022 Expressive Vocalizations Workshop and Competition: Recognizing, Generating, and Personalizing Vocal Bursts](https://mlsanity.com/paper/2205.01780/) (2022)
- [The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression](https://mlsanity.com/paper/2207.03572/) (2022)
- [Exploring the Effectiveness of Self-supervised Learning and Classifier Chains in Emotion Recognition of Nonverbal Vocalizations](https://mlsanity.com/paper/2206.10695/) (2022)
- [Comparing supervised and self-supervised embedding for ExVo Multi-Task learning track](https://mlsanity.com/paper/2206.11968/) (2022)
- [Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction](https://mlsanity.com/paper/1706.02757/) (2017)
- [Burst2Vec: An Adversarial Multi-Task Approach for Predicting Emotion, Age, and Origin from Vocal Bursts](https://mlsanity.com/paper/2206.12469/) (2022)
- [Self-supervision and Learnable STRFs for Age, Emotion, and Country Prediction](https://mlsanity.com/paper/2206.12568/) (2022)
- [Synthesizing Personalized Non-speech Vocalization from Discrete Speech Representations](https://mlsanity.com/paper/2206.12662/) (2022)
- [Multitask vocal burst modeling with ResNets and pre-trained paralinguistic Conformers](https://mlsanity.com/paper/2206.12494/) (2022)

## Built on by (most cited first, 1 of 1)

- [Non-Verbal Vocalisations and their Challenges: Emotion, Privacy, Sparseness, and Real Life](https://mlsanity.com/paper/2508.01960/) (2025)

## Abstract (arXiv)

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.