The paper evaluates audio‑visual predictive turn‑taking models trained on clean data when applied to a noisy cocktail‑party scenario derived from the AVCocktail dataset. Results show a consistent performance drop—up to 38% relative in weighted F1—across both audio and visual modalities, with fine‑tuning improving robustness but varying by modality and pre‑training data size. The study highlights differing generalisation and adaptation abilities of audio versus visual inputs and underscores the need for robust modelling strategies in noisy human interactions.
arXiv:2609.14666v1 Announce Type: cross Abstract: Turn-taking is a fundamental component of spoken interaction, and while humans naturally rely on both verbal and non-verbal signals, dialogue systems...
arXiv:2606. 16731v1 Announce Type: cross Abstract: Current multiparty turn-taking models often rely on complex microphone arrays or multi-camera setups, limiting their applicability in human-robot interaction scenarios.
arXiv:2606. 16568v1 Announce Type: cross Abstract: Reliable turn-taking is essential for spoken dialogue systems.
arXiv:2609.10394v1 Announce Type: cross Abstract: Current audio-visual speech recognition (AVSR) benchmarks, like LRS3, rely heavily on clean, scripted and rehearsed speech. They fail to reflect the...
TurnBench is a new multi‑domain benchmark for evaluating turn‑taking dynamics in spoken dialogue. It comprises a 30‑hour hand‑labeled corpus of dyadic human conversations, a standardized evaluation protocol for end‑of‑turn and interruption detection, and covers six distinct interaction styles with triple annotation. The benchmark also provides a 104‑hour training set, a public leaderboard, and an interactive dataset viewer at https://turnbench.sesame.com.
CIViC (Conditional Input and Visual Interpretation Checks) is a four‑gate protocol designed to link interpretation to intervention, held‑out task comparison, or endpoint control in audio‑conditioned visual prediction. The authors evaluate CIViC on three proxy tasks—actor‑disjoint face‑raster tests, a synthetic audio‑feature mixture, and source‑video‑disjoint SpeakerVid—showing that different gates reveal distinct effects of audio manipulation on visual outputs. Across these tasks, CIViC demonstrates that output activation, held‑out error, and endpoint controls can support varied interpretations of audio influence on visual predictions.
Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot must anticipate conversational dynamics rather than merely react to pauses. This work presents a Multimodal Voice Activity Projection (MM-VAP) framework that extends the original audio-only VAP formulation to synchronized audio-visual inputs while preserving its self-supervised future-projection objective.
The paper introduces AV-STE, a modular streaming audio‑visual front‑end that enhances corrupted semantic speech tokens using noisy audio and lip video before they reach a frozen speech LLM. By preserving the downstream dialogue model’s pretrained conversational abilities, AV‑STE improves response coherence from 1.42 to 1.91 in same‑dataset speaker interference scenarios while maintaining turn‑taking behavior. These gains also transfer to out‑of‑domain Seamless Interaction.
arXiv:2508. 08237v4 Announce Type: replace-cross Abstract: The emergence of audio-visual foundation models underscores the importance of reliably assessing their multi-modal understanding.
arXiv:2606. 15751v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) have shown remarkable success in zero-shot audio classification by aligning audio waveforms with text.
The paper introduces a method for controlling the turn‑taking behavior of full‑duplex spoken language models by identifying a single late‑layer residual‑stream direction, called the initiative vector, that determines how much the model speaks. By extracting this vector from the model’s own conversations without additional training, the authors can anticipate speech onsets up to 800 ms ahead and adjust the speech ratio from 6–12 % to 42–61 % through scaling during generation. A short reference recording of a target speaker allows the model to adopt that speaker’s turn‑taking style, and the transfer is highly speaker‑specific, with mismatched references degrading performance in 93–98 % of cases.
arXiv:2607. 07294v1 Announce Type: cross Abstract: Turn-taking prediction is a key requirement for social robots involved in human-human interaction, particularly in mediator settings, where the robot must anticipate conversational dynamics rather than merely react to pauses.