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

2025-08-03

AI Summary

Modeling nonverbal vocalizations (NVVs) faces core challenges: scarcity of real-world data, stringent privacy constraints, low ecological validity of experimenter-elicited samples, and decontextualized annotation. To address these, this study proposes a corpus-driven paradigm for NVV modeling, integrating psychological and linguistic frameworks. We design a privacy-preserving, naturalistic data collection strategy that transcends the limitations of prompted elicitation. Focusing on the prototypical NVV “ah”, we systematically characterize its affective functions, pragmatic types, and diachronic evolution. Our contributions are threefold: (1) a clarified research landscape and identification of critical bottlenecks in NVV science; (2) the first methodology for constructing ecologically valid, context-rich NVV corpora; and (3) foundational theory and scalable technical pathways enabling embodied, context-aware nonverbal affect modeling in AI systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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 extit{ah}. 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.

Problem

• Addressing challenges in Non-Verbal Vocalisations research
• Overcoming privacy and ethical issues in NVV data collection
• Improving realistic modelling of NVVs in real-life contexts

Innovation

• Corpus-based approaches for realistic NVV modeling
• Addressing privacy and ethical concerns in NVV research
• Overcoming sparsity in non-verbal vocalisation data