SignSpeak: Open-Source Time Series Classification for ASL Translation

TL;DR

This paper introduces SignSpeak, an open-source, real-time ASL translation system using a low-cost glove and a comprehensive dataset, achieving high accuracy and facilitating accessible sign language communication.

Contribution

It presents a novel low-cost glove, an extensive dataset, and benchmarks multiple models for real-time ASL translation, advancing accessibility tools.

Findings

Best model achieved 92% accuracy

Dataset contains 7200 samples across 36 classes

Open-source tools enable accessible ASL translation

Abstract

The lack of fluency in sign language remains a barrier to seamless communication for hearing and speech-impaired communities. In this work, we propose a low-cost, real-time ASL-to-speech translation glove and an exhaustive training dataset of sign language patterns. We then benchmarked this dataset with supervised learning models, such as LSTMs, GRUs and Transformers, where our best model achieved 92% accuracy. The SignSpeak dataset has 7200 samples encompassing 36 classes (A-Z, 1-10) and aims to capture realistic signing patterns by using five low-cost flex sensors to measure finger positions at each time step at 36 Hz. Our open-source dataset, models and glove designs, provide an accurate and efficient ASL translator while maintaining cost-effectiveness, establishing a framework for future work to build on.