Fillers in Spoken Language Understanding: Computational and Psycholinguistic Perspectives
It provides a holistic overview for the SLU community, but is incremental as it synthesizes existing perspectives without introducing novel methods or data.
The paper surveys research on fillers (e.g., 'uh', 'um') in spoken language, addressing their computational and psycholinguistic aspects to inform Spoken Language Understanding (SLU) and Conversational AI, but does not present new experimental results or concrete numbers.
Disfluencies (i.e. interruptions in the regular flow of speech), are ubiquitous to spoken discourse. Fillers ("uh", "um") are disfluencies that occur the most frequently compared to other kinds of disfluencies. Yet, to the best of our knowledge, there isn't a resource that brings together the research perspectives influencing Spoken Language Understanding (SLU) on these speech events. This aim of this article is to survey a breadth of perspectives in a holistic way; i.e. from considering underlying (psycho)linguistic theory, to their annotation and consideration in Automatic Speech Recognition (ASR) and SLU systems, to lastly, their study from a generation standpoint. This article aims to present the perspectives in an approachable way to the SLU and Conversational AI community, and discuss moving forward, what we believe are the trends and challenges in each area.