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REVIEW 4 major objections 5 minor 21 references

Prompt-Guided Turn-Taking Prediction

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Textual prompts like “faster” or “calmer” can steer a turn-taking predictor’s timing, and adding them also improves prediction accuracy.

desk verdict A genuinely new prompt-conditioning architecture for turn-taking prediction, but the headline accuracy gain is compromised by within-window label leakage and needs a corrected evaluation. read the letter →

arxiv 2506.21191 v2 pith:OUITMCIJ submitted 2025-06-26 cs.CL cs.SDeess.AS

Koji Inoue , Mikey Elmers , Yahui Fu , Zi Haur Pang , Divesh Lala , Keiko Ochi , Tatsuya Kawahara This is my paper · ORCID
classification cs.CLcs.SDeess.AS
keywords turn-takingpredictionvoiceactivityprojectiontextualpromptsspokendialoguesystemsconversationalrobotsprompt-conditionedmodelLLMsyntheticVAP
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper establishes that turn-taking prediction in spoken dialogue systems can be controlled by natural-language prompts instead of fixed model behavior. It extends the voice activity projection (VAP) transformer so that a short instruction for each participant, embedded as text, is injected into both the per-channel self-attention and the cross-channel attention stages. On roughly 950 hours of Japanese human-human dialogue, with prompts synthesized by an LLM from utterance timing, the prompt-conditioned model achieves lower VAP loss (2.346 vs. 2.431) and higher balanced accuracy (79.80% vs. 77.17%) than the original VAP model. Qualitative examples show that swapping prompts such as “respond immediately” and “speak slowly and deliberately” visibly shifts when the system is predicted to take its turn. The practical interest is that dialogue systems and robots could be adapted to users and contexts simply by changing an instruction.

What carries the argument

The central object is the voice activity projection (VAP) transformer, a model that maps two channels of speech audio through a contrastive predictive coding (CPC) encoder, per-channel self-attention, and a cross-channel attention transformer to predict voice activity over the next two seconds. The action is the prompt-conditioning mechanism: a 1792-dimensional sentence embedding of each participant's instruction is linearly projected and concatenated with the audio features at two depths, and a reconstruction head enforces that the prompt signal survives to the cross-attention output. That dual injection plus prompt reconstruction is what lets the same underlying predictor change its turn-taking timing when the text changes.

What would settle it

Take a fixed real-world instruction such as “respond faster and take the lead” and feed it with test audio segments whose actual style varies; if the predicted turn times do not move systematically toward faster responses, or if reassigning each audio segment to a randomly chosen prompt leaves VAP loss and balanced accuracy unchanged, then the reported gains come from prompt-segment leakage rather than genuine prompt understanding.

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Extended reading notes

Core claim

The central claim is that textual prompts can be turned into a first-class conditioning signal for turn-taking prediction. The authors build on the VAP architecture, which predicts future voice activity from a stereo audio stream, and concatenate prompt embeddings into the feature vectors at two points: right after the audio encoder and again after the per-channel self-attention transformer, before cross-channel attention. An auxiliary loss reconstructs the prompt embedding from the cross-attention output, so prompt information is pushed through the whole network. Trained with synthetic prompts generated by GPT-4.1 from the utterance timing in each audio segment, the model improves over the baseline on both VAP loss and balanced accuracy, and the paper demonstrates in worked examples that the predicted timing of system turns moves in the direction requested by the prompt. To the authors' knowledge, this is the first system to explicitly direct turn-taking prediction with textual prompts.

Load-bearing premise

The load-bearing assumption is that LLM-written prompts derived from each audio segment's speaking times behave like real instructions a user would give, so the measured controllability and accuracy gain are not artifacts of the prompt leaking information about the segment that the audio encoder cannot see.

Editorial extensions

If this is right

  • A dialogue system can reconfigure its own turn-taking style at runtime by swapping the prompt for the system channel, without retraining or changing the audio encoder.
  • Because prompts are conditioned per participant, the system can hold different behavioral specifications for the user and for itself in the same conversation.
  • If the accuracy gain is real, prompt text carries information about upcoming speech activity that the audio encoder alone does not extract, making prompts a useful auxiliary input rather than just a control knob.
  • The training recipe—synthesize prompts from timing statistics, then train with a reconstruction loss—is reusable for any turn-taking dataset that lacks natural prompt annotations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: synthetic prompts are generated from the same 20-second audio window they are applied to, so the prompt may leak information about that segment's timing that a real user issuing an instruction in advance would not have; a fixed instruction like “respond faster” might not shift behavior as cleanly as the examples suggest.
  • Beyond the paper: a natural next step is to test transfer with held-out prompt styles or with no prompt at inference time, to see whether the model falls back to baseline behavior or distorts predictions when the prompt is absent.
  • Beyond the paper: the same conditioning mechanism could plausibly be ported to multilingual or backchannel-prediction variants of VAP, where different prompt languages or listener styles would be desirable, but that would require collecting natural prompt data in each language.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper extends the Voice Activity Projection (VAP) turn-taking model with textual prompt conditioning. Prompt embeddings are produced by a Japanese text encoder, linearly projected, and concatenated at two points in the transformer stack (post-audio-encoder and post-self-attention), with an auxiliary loss that reconstructs the prompt embedding. Because no natural prompt annotations exist in the dialogue corpora, the authors generate synthetic prompts with GPT-4.1 from voice activity timings of the same 20-second audio segments used as model input. Experiments on approximately 950 hours of Japanese dialogues compare the proposed model with the original VAP and report lower VAP loss (2.346 vs. 2.431) and higher turn-shift/hold balanced accuracy (79.80% vs. 77.17%). Two qualitative examples show that swapping user and system prompts changes predicted turn-taking timing.

Significance. If the reported effects are real, prompt-controllable turn-taking would be a practically useful capability for spoken dialogue systems, and the proposed architecture is a simple, clearly described extension of a well-known baseline. The use of over 950 hours of diverse Japanese dialogue data and the explicit treatment of ethical considerations are strengths. However, the central quantitative comparison is compromised because the synthetic prompt is generated from the same audio window whose future voice activity is the prediction target; the accuracy gain over the original VAP can therefore be explained by information leakage rather than by prompt conditioning. The controllability claim rests on two qualitative examples without a quantitative metric. These issues make the current evidence insufficient, but they are addressable with additional experiments.

major comments (4)
  1. [Section 3.2, Table 1] The prompt-generation procedure creates a leakage path that invalidates the headline accuracy comparison. Section 3.2 states that GPT-4.1 is given the speaker identities and utterance start/end times for each 20-second audio segment aligned with the VAP model's training input, and that the prompt is generated from a description of that same segment's turn-taking style. The VAP prediction target is precisely the future voice activity derived from those utterance timings. Thus the proposed model receives, at every frame, a textual summary of the ground-truth information it is asked to predict, whereas the original VAP baseline receives only the audio. The improvement in Table 1 (VAP loss 2.346 vs. 2.431; S/H balanced accuracy 79.80 vs. 77.17) is therefore not evidence that textual prompts improve turn-taking prediction; it can be fully explained by target leakage. To support the claim, the authors should generate prompts from a preceding or held-out window, or otherwise ensure the prompt contains no information about the prediction target, and should compare against a baseline that receives the same auxiliary timing information in a non-textual form.
  2. [Section 4, Table 1] The evaluation uses a single random 8:1:1 session-level split with no error bars, no significance testing, and no multiple-seed analysis. Given that the reported differences are small (1.6 percentage points in balanced accuracy and 0.085 in VAP loss), the reader cannot determine whether these differences are stable or within run-to-run variability. The authors should report mean and standard deviation over several splits or training runs, and ideally a paired significance test, before claiming that the proposed model improves prediction accuracy.
  3. [Section 4, Figures 2 and 3] The qualitative demonstration of prompt controllability rests on exactly two examples with hand-prepared prompts, and no quantitative controllability metric is reported. The claim that the model 'effectively varied turn-taking timing behaviors according to the textual prompts' requires a more systematic evaluation, for example measuring turn-shift rate or response latency under systematically varied prompts across many test segments, and showing that the effect is statistically distinguishable from prompt-independent variation.
  4. [Section 2.2, Eq. (1)] The training loss in Eq. (1) sums L_vap, L_vad, and L_prompt with no stated weights. If the three terms are simply summed with equal weight, this should be stated; if weights were tuned, the values and selection procedure should be reported. More importantly, because the prompt embedding is concatenated at every frame and a reconstruction loss encourages the model to retain it, the model may learn to rely on the prompt rather than on the audio; the current experiment cannot separate these contributions because the prompt is generated from the audio's own future activity.
minor comments (5)
  1. [Section 2.2] The text says prompts are provided 'for each participant independently,' but Figure 1 shows a single 'Prompt for participant 1' path and mentions that the same processing is applied to participant 2; please clarify whether each participant has a distinct prompt and how the two prompts are encoded and combined when they differ.
  2. [Section 4] The statement that 'Model configurations and training parameters were consistent with those described in the original work' is not sufficient for reproducibility because the original VAP paper does not define the prompt-related hyperparameters introduced here, such as the prompt embedding projection dimension, the learning rate, batch size, number of epochs, or the loss weighting; please provide a complete hyperparameter table.
  3. [Appendix A] The example utterance timing contains overlapping intervals (e.g., B 13.813-16.725 and A 15.123-15.898), which is surprising for a VAD-based annotation of a dyadic conversation; please clarify whether this is intentional overlapped speech, a formatting artifact, or an error in the example.
  4. [Section 2.1] The description says VAP logits represent a probability distribution over 256 discrete states corresponding to four future time bins, but the aggregation into p_now and p_future is not formally defined; please specify how the 256 states map to the 0-600 ms and 600-2000 ms intervals.
  5. [Limitations] The Limitations section acknowledges the reliance on synthetic prompts and the restriction to Japanese data, but it does not acknowledge the more serious issue that the prompts are generated from the same audio segment whose future voice activity is the prediction target; this should be discussed explicitly and addressed experimentally.

Circularity Check

1 steps flagged · score 6.0 of 10

Quantitative gain is inflated by target leakage: prompts are synthesized from the same 20-second window whose future voice activity is the prediction target.

  1. self definitional [Section 3.2 (Prompt Generation); evaluation in Section 4, Table 1]
    "we synthetically generated prompt sentences from the dialogue data using a large language model (LLM). Specifically, we provided GPT-4.1 with voice activity details, including speaker identities and utterance timing (start and end times) for each 20-second audio segment aligned with the VAP model's training input format. ... The prompt sentences generated using this method were employed for both training and evaluating the proposed model."

    VAP predicts future voice activity in four bins (0-200ms to 1200-2000ms) from each frame of the 20-second segment. The prompt is generated from the utterance start/end times of that entire segment, so it is a textual summary of the same future voice activity the VAP head must predict (e.g., 'Include brief silences before and after your utterances'). The proposed model receives this summary at every frame while the original VAP baseline receives only audio, so the Table 1 gain (2.346 vs 2.431; 79.80 vs 77.17) can be explained by target leakage, not by prompt conditioning. The Limitations note acknowledges synthetic prompts but not this within-window leakage.

full rationale

The central quantitative claim (prompt conditioning improves turn-taking prediction accuracy) reduces, by the paper's own prompt-generation procedure, to giving the model a text description of the segment's utterance timing, which includes the future voice activity being predicted. This is a self-definitional leakage: the prompt is defined from the same target variable the model is asked to predict. The qualitative prompt-swap demonstrations (Figures 2-3) with manually supplied prompts are not themselves circular and show some prompt-conditioned modulation, but they are anecdotal and do not quantify controllability, so they do not rescue the quantitative comparison. No other circularity was found: the transformer/VAP architecture is taken from prior work as a baseline, not invoked as a uniqueness argument; self-citations are references to prior datasets/models and are not load-bearing for the prompt-control claim. Score 6 reflects partial circularity: the accuracy improvement is compromised by target leakage, while the controllability idea retains independent content.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The model introduces no new physical objects or external benchmarks. Its central claim rests on the assumption that LLM-generated synthetic prompts behave like real instructions, on the design choice of where to inject the prompt, and on the VAP base model. The free parameters are standard architecture choices rather than fitted constants, though the equal loss weighting is an unstated assumption.

free parameters (3)
  • Loss weights (lambda_vap, lambda_vad, lambda_prompt) = 1, 1, 1 (implicit)
    Equation (1) sums Lvap, Lvad, and Lprompt without stated weights. The equal weighting is a hyperparameter choice not justified or swept.
  • Prompt embedding projection dimension = 256
    The linear projection maps the 1792-dimensional prompt embedding to 256 to match the transformer input dimension. This value is chosen by hand, not experimentally optimized.
  • Audio window size = 20 seconds
    Inherited from the VAP model's training input format. The paper does not examine sensitivity to this window.
assumptions (4)
  • domain assumption The CPC audio encoder features are frozen and sufficient for turn-taking prediction
    The base VAP model freezes the CPC encoder, and the paper builds on this without re-examining whether the frozen features are sufficient. Section 2.1.
  • ad hoc to paper LLM-generated prompts can serve as a proxy for natural user instructions
    The prompts used for training and evaluation are generated from the same audio segments by GPT-4.1. The paper's limitations section acknowledges this is artificial and may limit generalizability.
  • ad hoc to paper Concatenating prompt embeddings at two points in the transformer is sufficient to condition the model on prompt information
    The architecture is designed this way, but no ablation or comparison to other conditioning mechanisms (e.g., cross-attention with prompt tokens) is provided. Section 2.2.
  • domain assumption Providing a text summary of recent turn-taking style helps predict future voice activity
    The quantitative result in Table 1 is consistent with this, but the comparison baseline lacks the same auxiliary summary, so the improvement might be due to extra information rather than text control specifically.

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Cite this review

Pith. "Pith review of Prompt-Guided Turn-Taking Prediction." pith.science (2026). https://pith.science/paper/OUITMCIJ

@misc{pith2026250621191,
  author       = {Pith},
  title        = {Pith review of: Prompt-Guided Turn-Taking Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OUITMCIJ}},
  note         = {Machine review of arXiv:2506.21191}
}
read the original abstract

Turn-taking prediction models are essential components in spoken dialogue systems and conversational robots. Recent approaches leverage transformer-based architectures to predict speech activity continuously and in real-time. In this study, we propose a novel model that enables turn-taking prediction to be dynamically controlled via textual prompts. This approach allows intuitive and explicit control through instructions such as "faster" or "calmer" adapting dynamically to conversational partners and contexts. The proposed model builds upon a transformer-based voice activity projection (VAP) model, incorporating textual prompt embeddings into both channel-wise transformers and a cross-channel transformer. We evaluated the feasibility of our approach using over 950 hours of human-human spoken dialogue data. Since textual prompt data for the proposed approach was not available in existing datasets, we utilized a large language model (LLM) to generate synthetic prompt sentences. Experimental results demonstrated that the proposed model improved prediction accuracy and effectively varied turn-taking timing behaviors according to the textual prompts.

Figures

Figures reproduced from arXiv: 2506.21191 by the authors.

Figure 1
Figure 1. Architecture of the proposed model. The prompt processing (illustrated on the left side) is also applied to those of participant 2. where each channel corresponds to one participant, in the manner of full-duplex systems. Each audio channel is processed by a pre-trained audio encoder using contrastive predictive coding (CPC) (Riv￾iere et al., 2020). The encoder parameters are frozen while training the VAP. The encode… view at source ↗
Figure 2
Figure 2. Example output of the proposed method. The top row is a waveform representation of the user’s audio [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Example output where the input prompts are opposite to those presented in Figure [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

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Works this paper leans on

21 extracted references · 17 canonical work pages

  1. [1]

    Angus Addlesee and Ioannis Papaioannou. 2025. Building for speech: designing the next-generation of social robots for audio interaction. Frontiers in Robotics and AI, 11:1--5

    work page 2025

  2. [2]

    Anderson and Campbell Leaper

    Kristin J. Anderson and Campbell Leaper. 1998. https://doi.org/10.1023/A:1018802521676 Meta-analyses of gender effects on conversational interruption: Who, what, when, where, and how . Sex Roles, 39(3-4):225--252

    work page doi:10.1023/a:1018802521676 1998

  3. [3]

    Erik Ekstedt and Gabriel Skantze. 2020. T urn GPT : a transformer-based language model for predicting turn-taking in spoken dialog. In International Conference on Empirical Methods in Natural Language Processing (EMNLP) Findings, pages 2981--2990

    work page 2020

  4. [4]

    Erik Ekstedt and Gabriel Skantze. 2022. Voice Activity Projection : Self-supervised learning of turn-taking events. In INTERSPEECH, pages 5190--5194

    work page 2022

  5. [5]

    Zhifang Guo, Yichong Leng, Yihan Wu, Sheng Zhao, and Xu Tan. 2023. PromptTTS : C ontrollable text-to-speech with text descriptions. In International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1--5

    work page 2023

  6. [6]

    Michimasa Inaba, Yuya Chiba, Ryuichiro Higashinaka, Kazunori Komatani, Yusuke Miyao, and Takayuki Nagai. 2022. Collection and analysis of travel agency task dialogues with age-diverse speakers. In International Conference on Language Resources and Evaluation (LREC), pages 5759--5767

    work page 2022

  7. [7]

    Koji Inoue, Bing ' er Jiang, Erik Ekstedt, Tatsuya Kawahara, and Gabriel Skantze. 2024. Multilingual turn-taking prediction using voice activity projection. In Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING), pages 11873--11883

    work page 2024

  8. [8]

    Koji Inoue, Divesh Lala, Gabriel Skantze, and Tatsuya Kawahara. 2025 a . Y eah, U n, O h: Continuous and real-time backchannel prediction with fine-tuning of voice activity projection. In North American Chapter of Association for Computational Linguistics (NAACL), pages 7171--7181

    work page 2025

Show all 21 references
  1. [9]

    Koji Inoue, Yuki Okafuji, Jun Baba, Yoshiki Ohira, Katsuya Hyodo, and Tatsuya Kawahara. 2025 b . A noise-robust turn-taking system for real-world dialogue robots: A field experiment. arXiv preprint, arXiv:2503.06241

    2025 arXiv

  2. [10]

    Hatim Khouzaimi, Romain Laroche, and Fabrice Lefevre. 2015. Optimising turn-taking strategies with reinforcement learning. In Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 315--324

    2015

  3. [11]

    Stephen C Levinson and Francisco Torreira. 2015. Timing in turn-taking and its implications for processing models of language. Frontiers in psychology, 6:731

    2015

  4. [12]

    Andreas Liesenfeld, Gabriella Parti, Yu-Yin Hsu, and Chu-Ren Huang. 2020. Predicting gender and age categories in english conversations using lexical, non-lexical, and turn-taking features. In Pacific Asia Conference on Language, Information and Computation (PACLIC), pages 22--30

    2020

  5. [13]

    Vânia Lourenço, Joana Serra, Joana Coutinho, and Alfredo F. Pereira. 2023. https://doi.org/10.1016/j.cognition.2023.105568 Turn-taking in free-play interactions: A cross-sectional study from 3 to 5 years . Cognition, 239:105568

    2023

  6. [14]

    Morgane Riviere, Armand Joulin, Pierre-Emmanuel Mazar \'e , and Emmanuel Dupoux. 2020. Unsupervised pretraining transfers well across languages. In International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 7414--7418

    2020

  7. [15]

    Gabriel Skantze. 2021. Turn-taking in conversational systems and human-robot interaction: A review. Computer Speech & Language, 67:101178

    2021

  8. [16]

    Gabriel Skantze and Bahar Irfan. 2025. Applying general turn-taking models to conversational human-robot interaction. In International Conference on Human-Robot Interaction (HRI), pages 859--868

    2025

  9. [17]

    Ming-Hsiang Su, Chung-Hsien Wu, and Yu-Ting Zheng. 2016. Exploiting turn-taking temporal evolution for personality trait perception in dyadic conversations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(4):733--744

    2016

  10. [18]

    Mark Ter Maat, Khiet P Truong, and Dirk Heylen. 2011. How agents' turn-taking strategies influence impressions and response behaviors. Presence: Teleoperators and Virtual Environments, 20(5):412--430

    2011

  11. [19]

    Lucien Tisserand, Brooke Stephenson, Heike Baldauf-Quilliatre, Mathieu Lefort, and Fr \'e d \'e ric Armetta. 2024. Unraveling the thread: understanding and addressing sequential failures in human-robot interaction. Frontiers in Robotics and AI, 11:1--19

    2024

  12. [20]

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Reviewed August 6, 2026 · model on record in the stance chip above.