arxiv: 2604.05605 · v1 · submitted 2026-04-07 · 💻 cs.CE · cs.AI· cs.CL· cs.CV· cs.ET

Recognition: no theorem link

INTERACT: An AI-Driven Extended Reality Framework for Accesible Communication Featuring Real-Time Sign Language Interpretation and Emotion Recognition

Nikolaos D. Tantaroudas , Andrew J. McCracken , Ilias Karachalios , Evangelos Papatheou

Authors on Pith no claims yet

Pith reviewed 2026-05-10 18:37 UTC · model grok-4.3

classification 💻 cs.CE cs.AIcs.CLcs.CVcs.ET

keywords extended realitysign language interpretationaccessibilityemotion recognitionreal-time translationvirtual meetingsdeaf communicationAI avatars

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The pith

INTERACT combines speech-to-text, sign-language avatars, translation, and emotion detection inside an immersive XR environment to support deaf and multilingual users in real-time meetings.

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

The paper introduces an XR platform called INTERACT that turns ordinary video calls into accessible spaces by automatically converting speech to text, rendering International Sign Language through 3D avatars, translating across languages, and detecting speaker emotions. It addresses the fact that over 430 million people live with disabling hearing loss and that current accessibility tools remain expensive or unavailable. Built on existing AI components and tested on consumer headsets, the system reports strong pilot results with technical experts and deaf participants. If the approach scales, it could remove key barriers that now exclude many people from professional, educational, and social interactions conducted online.

Core claim

INTERACT integrates Whisper for speech recognition, NLLB for translation, RoBERTa for emotion classification, and MediaPipe for gestures, then renders International Sign Language through 3D avatars inside a shared virtual room on Meta Quest 3 headsets. Pilot tests with technical experts followed by deaf community members produced 92 percent user satisfaction, transcription accuracy above 85 percent, 90 percent emotion-detection precision, and a mean experience rating of 4.6 out of 5, with 90 percent of participants willing to continue testing.

What carries the argument

A unified XR pipeline that fuses real-time speech-to-text, multilingual translation, emotion classification, and 3D avatar rendering of International Sign Language within a shared immersive environment.

If this is right

Video meetings become usable without external interpreters for deaf and hard-of-hearing participants.
Multilingual teams can participate in the same session with automatic translation and sign-language output.
Emotion cues become visible to users who cannot rely on facial expressions or tone alone.
The same architecture could support education and cultural events that currently exclude deaf attendees.

Where Pith is reading between the lines

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

The framework could be adapted for other sensory or cognitive accessibility needs by swapping the avatar and recognition modules.
Real-world deployment data would reveal whether latency or avatar naturalness limits adoption more than raw accuracy.
Integration with existing enterprise video tools would determine whether the headset requirement is a barrier or a feature.

Load-bearing premise

Results from small pilot groups of technical experts and deaf participants will hold when the system runs continuously with varied accents, lighting, and network conditions in everyday use.

What would settle it

A field trial in which transcription accuracy falls below 80 percent or emotion precision drops below 80 percent when participants use natural, unscripted speech in rooms with background noise.

Figures

Figures reproduced from arXiv: 2604.05605 by Andrew J. McCracken, Evangelos Papatheou, Ilias Karachalios, Nikolaos D. Tantaroudas.

**Figure 2.** Figure 2: Speech-to-Text Processing Pipeline illustrating the audio chunking, Whisper model [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Multilingual Translation Flow diagram depicting source text input, language detection, [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: International Sign Language gesture extraction pipeline. From left to right: original [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: 3D Avatar performing ISL signs within the virtual environment, showing the avatar [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Emotion Analysis Output displaying transcribed text with emotion labels and corre [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

**Figure 7.** Figure 7: Meeting Summarisation Workflow showing transcript accumulation, BART model [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗

**Figure 8.** Figure 8: Virtual Office Environment depicting the complete meeting space with participant [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗

**Figure 9.** Figure 9: Rainbow SDK Integration within Unity showing the SDK configuration and commu [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗

**Figure 10.** Figure 10: Avatar Animation and Emotion Analysis within the VR scene whilst hearing individ [PITH_FULL_IMAGE:figures/full_fig_p011_10.png] view at source ↗

**Figure 11.** Figure 11: Load Testing Performance Graphs depicting throughput, latency, and error rates [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗

read the original abstract

Video conferencing has become central to professional collaboration, yet most platforms offer limited support for deaf, hard-of-hearing, and multilingual users. The World Health Organisation estimates that over 430 million people worldwide require rehabilitation for disabling hearing loss, a figure projected to exceed 700 million by 2050. Conventional accessibility measures remain constrained by high costs, limited availability, and logistical barriers, while Extended Reality (XR) technologies open new possibilities for immersive and inclusive communication. This paper presents INTERACT (Inclusive Networking for Translation and Embodied Real-Time Augmented Communication Tool), an AI-driven XR platform that integrates real-time speech-to-text conversion, International Sign Language (ISL) rendering through 3D avatars, multilingual translation, and emotion recognition within an immersive virtual environment. Built on the CORTEX2 framework and deployed on Meta Quest 3 headsets, INTERACT combines Whisper for speech recognition, NLLB for multilingual translation, RoBERTa for emotion classification, and Google MediaPipe for gesture extraction. Pilot evaluations were conducted in two phases, first with technical experts from academia and industry, and subsequently with members of the deaf community. The trials reported 92% user satisfaction, transcription accuracy above 85%, and 90% emotion-detection precision, with a mean overall experience rating of 4.6 out of 5.0 and 90% of participants willing to take part in further testing. The results highlight strong potential for advancing accessibility across educational, cultural, and professional settings. An extended version of this work, including full pilot data and implementation details, has been published as an Open Research Europe article [Tantaroudas et al., 2026a].

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.

Desk Editor's Note private letter to a colleague

This is a practical XR integration paper that wires standard models into a Quest 3 app for real-time speech-to-ISL avatars plus emotion detection, with early positive pilot numbers but missing sample sizes and performance metrics.

read the letter

The main point is that INTERACT combines Whisper, NLLB, RoBERTa, and MediaPipe on the CORTEX2 framework to deliver speech-to-text, multilingual translation, International Sign Language avatar rendering, and emotion recognition inside Meta Quest 3 virtual meetings. They tested it in two phases, first with technical experts then with deaf community members, and report 92% satisfaction, above 85% transcription accuracy, 90% emotion precision, and a 4.6/5 experience score, with most users open to more testing. That is a legitimate end-to-end system description for an accessibility use case that matters to hundreds of millions of people with hearing loss. The architecture choices and deployment on consumer XR hardware are described clearly enough that someone building similar tools could follow the stack. The two-phase user testing sequence also makes basic sense. The soft spot is the evaluation. The reported percentages sit there without participant counts, task protocols, ground-truth collection methods, statistical tests, latency or FPS numbers for the full pipeline, or any baseline comparison to other sign-language or captioning systems. The text defers full implementation details and data to a 2026 extended version, so the current claims rest on aggregate figures that cannot be checked or reproduced from this manuscript. This work is aimed at applied researchers and developers in XR accessibility, HCI, or inclusive communication. A reader who wants concrete examples of how these open models fit together in a headset app will find usable pointers. Anyone needing rigorous benchmarks or generalizable performance data should wait for the fuller paper. I would send it to peer review because the integration itself is new enough and the accessibility goal is important, but the referees will need to press for the missing quantitative details before the results can be treated as solid evidence.

Referee Report

3 major / 3 minor

Summary. The manuscript presents INTERACT, an AI-driven XR framework for accessible communication that integrates real-time speech-to-text (Whisper), International Sign Language rendering via 3D avatars, multilingual translation (NLLB), and emotion recognition (RoBERTa) on Meta Quest 3 headsets using the CORTEX2 framework. Pilot evaluations in two phases (technical experts then deaf community members) report 92% user satisfaction, transcription accuracy above 85%, 90% emotion-detection precision, a 4.6/5 experience rating, and 90% willingness for further testing, with full details deferred to an extended 2026 version.

Significance. If the performance claims are substantiated, the work could advance inclusive XR tools for deaf, hard-of-hearing, and multilingual users by combining established AI components into an immersive platform, addressing WHO-noted gaps in accessibility. The practical deployment on consumer hardware and focus on real-time ISL avatars represent a useful engineering synthesis, though the absence of supporting data currently limits its assessed contribution.

major comments (3)

[Abstract / Pilot Evaluations] Abstract / Pilot Evaluations: The reported metrics (92% satisfaction, >85% transcription accuracy, 90% emotion precision, 4.6/5 rating) are presented without sample sizes, participant counts, task protocols, ground-truth measurement methods, statistical tests, baselines, or error analysis. These omissions directly undermine evaluation of the central claim that the integrated pipeline (Whisper + NLLB + RoBERTa + MediaPipe) delivers the stated performance.
[Abstract / System Description] Abstract / System Description: No latency, FPS, or end-to-end timing data are provided for the full real-time stack on Quest 3, nor any assessment of reliability under realistic conditions (e.g., varying speech, gestures, or environments). This leaves the 'real-time ISL avatar rendering' claim unsupported despite being load-bearing for the framework's contribution.
[Abstract] Abstract: The manuscript explicitly defers full pilot data and implementation details to [Tantaroudas et al., 2026a], yet the current text's quantitative claims cannot be assessed without them; this is not a minor omission given that the pilots constitute the primary evidence.

minor comments (3)

[Title] Title: Typo in 'Accesible' (should be 'Accessible').
[Abstract] Abstract: Brief citations or one-sentence descriptions of Whisper, NLLB, RoBERTa, and MediaPipe would improve accessibility for readers outside the immediate subfield.
[Abstract] Abstract: The transition between the two pilot phases and any differences in evaluation protocols could be clarified for reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thorough review and constructive comments. We address each major comment below and have made revisions to the manuscript to provide additional clarity and details where feasible, drawing from our extended work.

read point-by-point responses

Referee: [Abstract / Pilot Evaluations] Abstract / Pilot Evaluations: The reported metrics (92% satisfaction, >85% transcription accuracy, 90% emotion precision, 4.6/5 rating) are presented without sample sizes, participant counts, task protocols, ground-truth measurement methods, statistical tests, baselines, or error analysis. These omissions directly undermine evaluation of the central claim that the integrated pipeline (Whisper + NLLB + RoBERTa + MediaPipe) delivers the stated performance.

Authors: We agree that the current presentation of the pilot results is high-level and lacks the detailed methodological information needed for full assessment. The manuscript is a concise summary, with the complete pilot evaluations, including sample sizes (technical experts and deaf community members), protocols, ground-truth methods, and statistical analysis, provided in the extended Open Research Europe article [Tantaroudas et al., 2026a]. To address this, we will revise the manuscript to include a brief overview of the evaluation setup, participant numbers, and key methodological aspects in the Pilot Evaluations section, while maintaining the reference to the full details. revision: yes
Referee: [Abstract / System Description] Abstract / System Description: No latency, FPS, or end-to-end timing data are provided for the full real-time stack on Quest 3, nor any assessment of reliability under realistic conditions (e.g., varying speech, gestures, or environments). This leaves the 'real-time ISL avatar rendering' claim unsupported despite being load-bearing for the framework's contribution.

Authors: We acknowledge the importance of performance metrics for validating the real-time capabilities. The extended version includes detailed latency, FPS, and timing measurements for the integrated pipeline on Meta Quest 3, along with reliability assessments under various conditions. In the revised manuscript, we will incorporate key performance indicators, such as average end-to-end latency and frame rates, to better support the real-time claims in this version. revision: yes
Referee: [Abstract] Abstract: The manuscript explicitly defers full pilot data and implementation details to [Tantaroudas et al., 2026a], yet the current text's quantitative claims cannot be assessed without them; this is not a minor omission given that the pilots constitute the primary evidence.

Authors: The current manuscript is designed as an overview of the INTERACT framework, explicitly directing readers to the extended paper for comprehensive data and details. However, we recognize that this may not suffice for standalone evaluation. We will revise the abstract and relevant sections to provide more context on the pilots and include summarized quantitative results with basic supporting information, ensuring the claims are better substantiated while still referencing the full study. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical system description relies on external components and direct study reports

full rationale

The paper describes an XR accessibility platform built from established external libraries (Whisper for ASR, NLLB for translation, RoBERTa for emotion, MediaPipe for gestures) and reports aggregate pilot-study percentages (92% satisfaction, >85% transcription accuracy, 90% emotion precision) without any equations, fitted parameters, or derivations. The single self-citation to the authors' own extended 2026 version supplies supplementary data but is not invoked to justify uniqueness, ansatzes, or core claims; the performance figures are stated independently in the present text. No self-definitional loops, predictions that reduce to fitted inputs, or renamings of known results appear. The chain is therefore self-contained as architecture description plus empirical observation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an applied engineering paper with no mathematical model, free parameters, or new theoretical entities; it relies on standard assumptions about AI model performance and user-study validity.

pith-pipeline@v0.9.0 · 5641 in / 1301 out tokens · 66640 ms · 2026-05-10T18:37:41.777972+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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