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Synthetic Corpus Generation for Deep Learning-Based Translation of Spanish Sign Language.

Marina Perea-Trigo¹, Celia Botella-López², Miguel Ángel Martínez-Del-Amor^2,3

¹Department of Languages and Computer Systems, Universidad de Sevilla, 41012 Sevilla, Spain.

Sensors (Basel, Switzerland)

|March 13, 2024

Summary

This summary is machine-generated.

This study introduces ruLSE, a system for creating Spanish Sign Language datasets. Experiments show ruLSE

Keywords:

gloss neural machine translation sign language sign language production sign language translation synthetic corpus

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Area of Science:

Computer Science
Artificial Intelligence
Natural Language Processing

Background:

Sign language is vital for the deaf community.
Technological advancements are needed to bridge communication gaps between deaf and hearing individuals.
Current research focuses on sign language recognition, translation, and production.

Approach:

A rule-based system, ruLSE, was developed to generate synthetic Spanish Sign Language datasets.
Two Transformer-based models, MarianMT and Transformer-STMC, were evaluated using the generated datasets.
The effectiveness of pre-trained word embeddings in Spanish was also investigated.

Key Points:

MarianMT achieved better translation results (+$3.7 BLEU-4) than Transformer-STMC.
Transformer-STMC demonstrated higher efficiency, being up to four times faster.
The ruLSE system outperformed Transformer models in Sign Language Production tasks.
Pre-trained Spanish word embeddings improved overall results.

Conclusions:

The generated synthetic dataset, synLSE, is now publicly available.
The ruLSE system offers a superior and efficient approach to Sign Language Production.
This work advances sign language technology by providing valuable resources and insights.