A multimodal Turkish conversational dataset of unscripted dyadic interactions, comprising synchronized front-facing video, per-speaker audio channels that allow overlapping speech to be attributed to individual speakers, and time-aligned transcriptions is introduced.
Abstract
Turn-taking is a basic organizational feature of human conversation and remains difficult to model in natural, synchronous dialog systems. While existing research has explored multimodal approaches and large language models for turn-ending prediction, there is a lack of naturalistic conversational corpora specifically addressing turn-taking dynamics in Turkish. This study introduces a multimodal Turkish conversational dataset of unscripted dyadic interactions, comprising synchronized front-facing video, per-speaker audio channels that allow overlapping speech to be attributed to individual speakers, and time-aligned transcriptions. Turn-taking prediction is formulated as a binary classification problem, and a Genetic Algorithm (GA) is employed to optimize interpretable decision rules derived from visual, acoustic, and linguistic features. A hybrid AND-OR rule representation is adopted in the proposed framework to represent the alternative cue combinations that precede a turn transition.
TurnBench, a multi-domain benchmark that pairs a 30-hour, hand-labeled corpus of dyadic human conversation with a standardized evaluation protocol for end-of-turn and interruption detection, finds end-of-turn recall stable across types, while interruption false positives are strongly type-dependent and concentrated in...
Freeman Jiang, Ramon Sanabria, Soham Deshmukh et al.· 7 citations· ⚡2
Conversational and multimodal corpora are central to affective computing, yet their analysis remains challenging due to heterogeneous data formats, modality-specific signals, and interactional dynamics such as speaker turns and temporal variation. Existing approaches often rely on ad hoc toolchains, making workflows di...
Parth Kapur, Shubh Sehgal, Ajay Gopi et al.· 2026 14th International Conf...· 0 citations
The lack of non-verbal signals in text-based communication forces dialogue systems to rely entirely on dynamic textual sentiment changes to infer user intent and emotional shifts. However, substantial class imbalance and the static, post-hoc nature of existing text analysis models prevent dialogue systems from recogniz...
Mejdl S. Safran· International Islamic Univer...· 0 citations
This study forms continuous recognition of the Group Emotion at a one-second resolution, introduces the Mixed state, which captures the emotional divergence among participants in the group, and proposes a multimodal temporal framework that integrates audio and video information using a sliding-window context.
Soma Iwata, K. Inoue, Mu-Yun Wu et al.· Companion Publication of the...· 0 citations
This paper describes the collection design, participants, recording setup, transcription format, and corpus statistics, and provides preliminary analyses to illustrate how TEIDAN can support research on turn-taking, addressee recognition, and multimodal grounding in human-human and human-agent interaction.
Taiga Mori, K. Inoue, Mikey Elmers et al.· Companion Publication of the...· 2 citations
The evolution of linguistic style in conversations is an underexplored issue in NLP. Most style-control datasets focus on sentences or assume a static style throughout, missing the dynamic shifts that occur as user preferences change during interactions. We introduce ConvoDrift, a dataset designed to model progressive...
Vihindi Kotalawala, Pamoda Dilranga, Gayani Thoradeniya et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
