Year: 2026
Type: conference-paper
Abstract: Backchannels (e.g., 'yeah', 'mhm', and 'right') are short, non-interruptive feedback signals whose lexical form and prosody jointly convey pragmatic meaning.While prior computational research has largely focused on predicting backchannel timing, the relationship between lexico-prosodic form and meaning remains underexplored.We propose a two-stage framework: first, fine-tuning large language models on dialogue transcripts to derive rich contextual representations; and second, learning a joint embedding space for dialogue contexts and backchannel realizations.We evaluate alignment with human perception via triadic similarity judgments (prosodic and cross-lexical) and a context-backchannel suitability task.Our results demonstrate that the learned projections substantially improve context-backchannel retrieval compared to previous methods.In addition, they reveal that backchannel form is highly sensitive to extended conversational context and that the learned embeddings align more closely with human judgments than raw WavLM features.Pitch length: 89.1 Pitch range: 14.
Authors: Livia Qian, Gabriel Skantze
Institution: KTH Royal Institute of Technology
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166871475