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Updated: Jun 26, 2026

Portable Intermodal Preferential Looking IPL: Investigating Language Comprehension in Typically Developing Toddlers and Young Children with Autism

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Temporal Video Segmentation Approach for Peruvian Sign Language.

Summy Farfan¹, Juan J Choquehuanca-Zevallos^1,2, Ana Aguilera^3,4

¹Electrical and Electronics Engineering Department, Universidad Católica San Pablo, Arequipa 04001, Peru.

Sensors (Basel, Switzerland)

|September 13, 2025

Summary

This summary is machine-generated.

This study enhances continuous Sign Language Recognition by improving temporal segmentation. A diffusion-based model demonstrated superior performance in identifying individual signs and transitions in Peruvian Sign Language videos.

Keywords:

Peruvian Sign Language deep learning diffusion network temporal segmentation

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Area of Science:

Computer Science
Artificial Intelligence
Linguistics

Background:

Continuous Sign Language Recognition (CSLR) involves translating full sign language video sequences.
Temporal video segmentation is crucial for distinguishing signs from transitions in CSLR.
Current CSLR methods often use outdated architectures, limiting advancements.

Purpose of the Study:

To identify key characteristics differentiating signs from transitions in continuous sign language.
To adapt and evaluate modern temporal segmentation models for Sign Language.
To improve the accuracy and robustness of Sign Language recognition systems.

Main Methods:

Adapted two temporal segmentation models: DiffAct (diffusion-based) and MS-TCN.
Applied models to a precisely annotated dataset of Peruvian Sign Language.
Explored three training strategies: baseline, data augmentation, and multi-dataset.

Main Results:

Training strategies improved scores for both models but increased variability.
The diffusion-based model (DiffAct) showed better generalization to unseen sequences.
DiffAct achieved high scores in sign and transition identification (median mF1S: 71.89%, mF1B: 72.68%).

Conclusions:

Modern temporal segmentation models can be effectively applied to Sign Language recognition.
Diffusion-based approaches show promise for robust CSLR.
Further research can refine these methods for improved Sign Language understanding.