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arxiv: 2606.09424 · v1 · pith:OXHLMIKEnew · submitted 2026-06-08 · 💻 cs.CL

Toward Signing Activity Projection in Sign Language Interaction

Takao Obi , Wang Yusong , Koji Inoue , Kotaro Funakoshi This is my paper

Pith reviewed 2026-06-27 16:49 UTC · model grok-4.3

classification 💻 cs.CL
keywords sign language interactionturn-taking predictionsigning activity projectionhand cuesdyadic dialoguepose featuresproxy task
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The pith

Adapting spoken turn-taking models to sign language works for predicting hold-or-shift decisions when hand cues are available but struggles with shift prediction alone.

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

This paper tests whether a predictive framework originally built for voice activity in speech can transfer to sign language dyads. It derives binary signing activity streams from lexical annotations in an existing corpus and trains an adapted model on pose features from hands, eye regions, and mouth regions. The results indicate that combined shift-or-hold prediction succeeds especially when hand information is used, whereas isolated shift prediction does not. The work therefore supplies early evidence both that some transfer is feasible and that speech-derived event categories are insufficient for signing. It concludes that sign-language-specific definitions of interaction events will be needed for robust predictive modeling.

Core claim

Transferring the Voice Activity Projection architecture to dyadic sign language interaction yields promising accuracy on SHIFT/HOLD prediction when hand-derived features are included, while SHIFT prediction remains difficult; these outcomes demonstrate both the partial viability of the transfer and the limits of relying on speech-derived activity categories.

What carries the argument

An adapted Voice Activity Projection model that takes pose-derived hand, eye-region, and mouth-region features as input to forecast future binary signing activity states derived from lexical annotations.

If this is right

  • Hand cues supply the strongest signal for deciding whether the current signer will hold or yield the floor.
  • Combined hold-or-shift labels are easier to predict than shift labels alone under the current formulation.
  • Pose features alone can support initial predictive modeling without requiring full video or depth data.
  • Any successful deployment to robots will need event definitions that are native to signing rather than borrowed from speech.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Defining signing-specific events such as sustained gaze aversion or body lean could improve prediction beyond the current proxy labels.
  • The same architecture might be tested on multi-party signing or on interactions where one participant is a signing robot.
  • Collecting new corpora with explicit turn annotations rather than deriving them from lexical tiers would provide a stronger test of the approach.

Load-bearing premise

Binary streams of signing activity extracted from lexical sign annotations serve as a sufficient proxy for the turn-taking events that actually occur in live sign language conversations.

What would settle it

A follow-up experiment that records live sign language dyads, annotates actual turn transitions independently of lexical signs, and shows that the model trained on the proxy streams fails to predict those transitions would falsify the transfer claim.

Figures

Figures reproduced from arXiv: 2606.09424 by Koji Inoue, Kotaro Funakoshi, Takao Obi, Wang Yusong.

Figure 1
Figure 1. Figure 1: Example of sign language interaction in Public DGS [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Projection binning scheme. following the existing VAP preprocessing [2]. Table I re￾ports dataset statistics and the numbers of extracted event targets (not the number of 20 s windows) computed on the resulting splits. For SHIFT-prediction, negative samples are constructed to match the number of positives; we report only positive counts. Importantly, these labels represent signing activity rather than utte… view at source ↗
read the original abstract

Social robots must interact robustly not only with users assumed by speech-centered systems but also with diverse users whose communication relies on different modalities, e.g., sign language. One important capability gap is predictive turn-taking with signing users. Although Voice Activity Projection (VAP) has been successfully used to model future voice activity in spoken interaction, it remains unclear whether the framework transfers to sign language interaction. This paper presents an initial transfer study of adapting a VAP architecture to dyadic sign language interaction. Using interaction recordings from the Public DGS Corpus, we derive binary signing activity streams from lexical sign annotations and formulate proxy tasks for turn-taking prediction. The model uses pose-derived hand, eye-region, and mouth-region features extracted for each signer. The results show that SHIFT/HOLD prediction is promising, especially with hand cues, while SHIFT-prediction remains difficult. These findings provide initial evidence for both the promise and the current limitations of transferring predictive turn-taking models from spoken interaction to sign language interaction. Predictive modeling of sign language interaction still requires sign-language-specific event definitions that go beyond speech-derived categories.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents an initial transfer study adapting the Voice Activity Projection (VAP) framework to dyadic sign language interaction. Using recordings from the Public DGS Corpus, binary signing activity streams are derived from lexical sign annotations to formulate proxy tasks for turn-taking prediction (SHIFT/HOLD and SHIFT). The model incorporates pose-derived features from hands, eye regions, and mouth regions. Results indicate SHIFT/HOLD prediction is promising (especially with hand cues) while SHIFT prediction remains difficult, providing initial evidence for both the promise and limitations of transferring predictive turn-taking models from spoken interaction, with a call for sign-language-specific event definitions.

Significance. If the proxy tasks are shown to validly approximate interactional turn-taking, the work would be significant for extending predictive modeling to sign language users in human-robot interaction, addressing a modality gap in inclusive systems. It usefully identifies differential difficulty between prediction subtasks and demonstrates adaptation of a VAP-style architecture with multi-cue pose features. The explicit acknowledgment of limitations and need for domain-specific definitions is a strength. However, the current presentation supplies only directional claims without metrics, limiting assessment of practical impact.

major comments (2)
  1. [Abstract] Abstract: the claim that 'the results show that SHIFT/HOLD prediction is promising' supplies no quantitative metrics, baselines, error bars, or architecture/training details, leaving the central claim of 'initial evidence' unsupported by visible evidence and preventing evaluation of effect size or reliability.
  2. [Abstract] Abstract (proxy task formulation): binary signing activity streams derived from lexical sign annotations are used as proxies for turn-taking events without any reported alignment checks, human validation against interactional phenomena (holds, overlaps, gaze shifts), or comparison to established sign-language coding schemes; this untested mapping is load-bearing for the transfer claim.
minor comments (2)
  1. The abstract references 'pose-derived hand, eye-region, and mouth-region features' but omits extraction pipeline, feature dimensionality, or how they are fed into the VAP architecture.
  2. Consider citing prior literature on sign-language turn-taking or VAP extensions to non-speech modalities to better situate the transfer study.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive review of our initial transfer study. We address each major comment below and outline revisions to improve the clarity and support for our claims while preserving the exploratory nature of the work.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'the results show that SHIFT/HOLD prediction is promising' supplies no quantitative metrics, baselines, error bars, or architecture/training details, leaving the central claim of 'initial evidence' unsupported by visible evidence and preventing evaluation of effect size or reliability.

    Authors: We agree that the abstract would be strengthened by including key quantitative results. The full manuscript reports F1 scores, comparisons across feature sets (hands, eyes, mouth), and training details for the VAP-style model, but these are not summarized in the abstract. We will revise the abstract to report specific metrics (e.g., F1 for SHIFT/HOLD with hand cues) and briefly note the architecture and evaluation setup to make the central claims self-contained and evaluable. revision: yes

  2. Referee: [Abstract] Abstract (proxy task formulation): binary signing activity streams derived from lexical sign annotations are used as proxies for turn-taking events without any reported alignment checks, human validation against interactional phenomena (holds, overlaps, gaze shifts), or comparison to established sign-language coding schemes; this untested mapping is load-bearing for the transfer claim.

    Authors: We acknowledge this as a valid limitation of the current proxy formulation. The manuscript already notes the need for sign-language-specific event definitions beyond speech-derived categories. To address the concern, we will expand the discussion to explicitly describe the proxy derivation process, note the absence of alignment validation in this initial study, and outline plans for future human validation against interactional phenomena and established coding schemes. We maintain that the lexical-annotation proxy is a reasonable starting point given available data, but agree additional caveats are warranted. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical transfer study with independent evaluation

full rationale

The paper performs an empirical adaptation of the VAP framework to sign-language data by deriving binary activity streams from existing lexical annotations, extracting pose features, training a model, and reporting SHIFT/HOLD prediction metrics. No equations, fitted parameters, or self-citations are shown to reduce any reported prediction to the input labels by construction. The central results rest on held-out evaluation of the trained model rather than on definitional equivalence or load-bearing self-reference. The proxy-task assumption is an external validity concern, not a circularity in the derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, no fitted constants, and no explicit assumptions beyond the use of lexical annotations as activity proxies; ledger therefore empty.

pith-pipeline@v0.9.1-grok · 5720 in / 1097 out tokens · 15931 ms · 2026-06-27T16:49:35.443235+00:00 · methodology

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Reference graph

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