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arxiv: 2605.23328 · v1 · pith:QPMJQCYCnew · submitted 2026-05-22 · 💻 cs.CL

Emotion Recognition in Sign Language Conversation

Yusong Wang , Keyu Mao , Takao Obi , Minghao Shao , Kotaro Funakoshi This is my paper

Pith reviewed 2026-05-25 04:50 UTC · model grok-4.3

classification 💻 cs.CL
keywords sign languageemotion recognition in conversationdomain gapmultimodal modelsvisual extractorsconversational datasetsaffective computingeJSL Dialog dataset
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The pith

Generic multimodal emotion recognition models exhibit a domain gap on sign language conversations, requiring specialized context-aware visual extractors.

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

The paper extends emotion recognition in conversation to sign language videos by releasing the eJSL Dialog dataset built from STUDIES corpus scripts. This resource supplies 1920 video samples across 480 dialogues to overcome the limitation that prior sign language emotion data consists only of isolated sentences. Benchmarking experiments compare isolated visual networks against full multimodal conversational architectures and document consistent performance drops when standard models are applied to signing. The results establish that sign language requires visual extractors that explicitly incorporate dialogue context rather than generic multimodal pipelines. The work therefore identifies larger-scale conversational sign language corpora as a prerequisite for effective pre-training.

Core claim

The central claim is that generic multimodal conversational emotion recognition models display a domain gap when transferred to sign language, as shown by systematic benchmarking on the new eJSL Dialog dataset of 1920 videos in 480 dialogues; this gap demonstrates the explicit need for context-aware visual extractors tailored to sign language and for expanded conversational datasets that enable large-scale pre-training.

What carries the argument

The eJSL Dialog dataset of 1920 sign language video samples in 480 unique dialogues, used to benchmark models ranging from isolated visual networks to multimodal conversational architectures.

If this is right

  • Isolated-sentence sign language models lose accuracy once dialogue history is introduced.
  • Multimodal architectures trained on spoken-language data transfer poorly to signing without visual extractor changes.
  • Context must be modeled jointly with sign-specific visual features rather than added as an afterthought.
  • Future progress in sign language emotion recognition depends on scaling conversational video corpora for pre-training.
  • Real-world deployment of affective sign language systems will require extractors that process manual and non-manual signals across turns.

Where Pith is reading between the lines

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

  • The observed gap may arise because emotional cues in signing rely more heavily on simultaneous non-manual signals and spatial grammar than on sequential prosody.
  • Architectures originally developed for spoken ERC could be adapted by replacing their visual backbone with one pre-trained on large sign language corpora.
  • The same benchmarking approach could be applied to other under-resourced visual communication systems such as gesture-based or tactile languages.
  • If larger datasets close the gap, existing multimodal ERC codebases might be reused with only modest visual front-end changes.

Load-bearing premise

The eJSL Dialog dataset constructed from STUDIES corpus scripts is representative enough of real-world sign language conversational dynamics to support conclusions about domain gaps.

What would settle it

A controlled experiment in which a generic multimodal conversational model achieves parity with sign-language-adapted models on an independently collected, larger sign language dialogue corpus without any domain-specific modifications.

Figures

Figures reproduced from arXiv: 2605.23328 by Keyu Mao, Kotaro Funakoshi, Minghao Shao, Takao Obi, Yusong Wang.

Figure 1
Figure 1. Figure 1: Illustration of the sign language video recording process. The [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Emotion transition structure on eJSL Dialogue. Panel (A) reports [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Case studies illustrating complementary roles of conversational [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original abstract

Emotion Recognition in Conversation is a core component of affective computing, while current resources of sign language emotion datasets primarily focus on isolated sentences and lack conversational context. Models trained exclusively on these isolated utterances demonstrate degraded performance in real world scenarios because they cannot utilize historical dialogue flow. To address this structural limitation, we introduce the ERC task to sign language video analysis and propose the eJSL Dialog dataset. Constructed using the scripts from the STUDIES corpus, the dataset contains 1,920 video samples organized into 480 unique dialogues. We conduct systematic benchmarking on this dataset using models ranging from isolated visual networks to multimodal conversational architectures. The results reveal a domain gap when applying generic multimodal conversational emotion recognition models to sign language. These findings demonstrate the explicit need for context aware visual extractors specific to sign language and indicate that expanding the scale of conversational datasets to support large scale pre-training is a necessary next step for future research.

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

1 major / 1 minor

Summary. The paper introduces the ERC task to sign language by releasing the eJSL Dialog dataset (1,920 video samples in 480 dialogues) constructed from scripts of the STUDIES corpus. It benchmarks isolated visual networks through multimodal conversational models and reports a domain gap for generic ERC architectures on sign language data, concluding that context-aware visual extractors specific to sign language are required and that larger-scale conversational pre-training data are needed.

Significance. The dataset introduction fills a clear resource gap, as prior sign-language emotion datasets are limited to isolated utterances. If the domain-gap result is robust, the work usefully flags that off-the-shelf multimodal conversational models do not transfer directly and motivates sign-language-specific modeling. The empirical benchmarking itself is a concrete starting point, though its interpretive weight depends on dataset representativeness.

major comments (1)
  1. [Dataset Construction] Dataset Construction (abstract and §3): the dataset is assembled from scripted STUDIES corpus material. Scripted dialogues typically omit spontaneous turn-taking, prosodic variation, and co-speech gesture integration that characterize natural sign-language conversation. Any measured performance drop versus generic ERC models could therefore arise from artificiality rather than sign-language-specific visual features, weakening the claim that specialized context-aware extractors are demonstrably required.
minor comments (1)
  1. [Abstract] Abstract: the phrasing 'eJSL Dialog dataset' should be accompanied by a brief expansion or consistent acronym on first use to aid readers unfamiliar with the STUDIES corpus.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the single major comment below.

read point-by-point responses

  1. Referee: [Dataset Construction] Dataset Construction (abstract and §3): the dataset is assembled from scripted STUDIES corpus material. Scripted dialogues typically omit spontaneous turn-taking, prosodic variation, and co-speech gesture integration that characterize natural sign-language conversation. Any measured performance drop versus generic ERC models could therefore arise from artificiality rather than sign-language-specific visual features, weakening the claim that specialized context-aware extractors are demonstrably required.

    Authors: We agree that the scripted nature of the STUDIES corpus dialogues represents a limitation, as it may not fully reflect spontaneous turn-taking, prosody, or gesture integration present in natural sign-language conversation. This could indeed contribute to the observed performance differences and tempers the strength of the claim that the domain gap is attributable solely to sign-language-specific visual features. We will revise Section 3 to expand the discussion of dataset construction limitations and will moderate the abstract and conclusion statements to note that the results motivate sign-language-specific modeling while acknowledging the need for validation on spontaneous data. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical dataset introduction and benchmarking

full rationale

The paper introduces the eJSL Dialog dataset from the existing STUDIES corpus scripts and reports benchmarking results across visual and multimodal models. No derivations, equations, fitted parameters, or predictions are present that could reduce to inputs by construction. Claims about domain gap rest on direct experimental comparisons rather than self-referential definitions or self-citation chains. The work is self-contained against external benchmarks and falsifiable via new data collection.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the assumption that the STUDIES corpus provides suitable scripts for constructing representative conversational sign language data and that the selected benchmarking models adequately represent generic multimodal approaches. No free parameters or invented entities are evident from the abstract.

axioms (1)
  • domain assumption The STUDIES corpus scripts accurately represent natural sign language conversational dynamics for emotion labeling.
    Dataset is constructed using scripts from the STUDIES corpus.

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Reference graph

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