# Non-Verbal Vocalisations and their Challenges: Emotion, Privacy, Sparseness, and Real Life

arXiv: https://arxiv.org/abs/2508.01960 · HTML: https://mlsanity.com/paper/2508.01960/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 97 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [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)
- [Self-Consuming Generative Models Go MAD](https://mlsanity.com/paper/2307.01850/) (2023)
- [Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers](https://mlsanity.com/paper/2212.08570/) (2022)
- [ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets](https://mlsanity.com/paper/2406.10275/) (2024)
- [voc2vec: A Foundation Model for Non-Verbal Vocalization](https://mlsanity.com/paper/2502.16298/) (2025)
- [On Laughter and Speech-Laugh, Based on Observations of Child-Robot Interaction](https://mlsanity.com/paper/1908.11593/) (2019)
- [EmoGator: A New Open Source Vocal Burst Dataset with Baseline Machine Learning Classification Methodologies](https://mlsanity.com/paper/2301.00508/) (2023)

## Abstract (arXiv)

Non-Verbal Vocalisations (NVVs) are short `non-word' utterances without proper linguistic (semantic) meaning but conveying connotations -- be this emotions/affects or other paralinguistic information. We start this contribution with a historic sketch: how they were addressed in psychology and linguistics in the last two centuries, how they were neglected later on, and how they came to the fore with the advent of emotion research. We then give an overview of types of NVVs (formal aspects) and functions of NVVs, exemplified with the typical NVV textitah. Interesting as they are, NVVs come, however, with a bunch of challenges that should be accounted for: Privacy and general ethical considerations prevent them of being recorded in real-life (private) scenarios to a sufficient extent. Isolated, prompted (acted) exemplars do not necessarily model NVVs in context; yet, this is the preferred strategy so far when modelling NVVs, especially in AI. To overcome these problems, we argue in favour of corpus-based approaches. This guarantees a more realistic modelling; however, we are still faced with privacy and sparse data problems.