Summary of Signspeak: Open-source Time Series Classification For Asl Translation, by Aditya Makkar et al.
SignSpeak: Open-Source Time Series Classification for ASL Translation
by Aditya Makkar, Divya Makkar, Aarav Patel, Liam Hebert
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a low-cost, real-time American Sign Language (ASL) to speech translation glove, along with an exhaustive training dataset of sign language patterns. The SignSpeak dataset consists of 7200 samples encompassing 36 classes (A-Z, 1-10), capturing realistic signing patterns using five low-cost flex sensors. Supervised learning models, such as LSTMs, GRUs, and Transformers, were benchmarked on this dataset, with the best model achieving 92% accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps people who are deaf or hard of hearing communicate more easily by creating a device that translates American Sign Language (ASL) into spoken language. The researchers also made a big collection of ASL signs and patterns to train a computer program to recognize these signs. They tested different types of machine learning models on this data and found one that works really well, achieving 92% accuracy. |
Keywords
* Artificial intelligence * Glove * Machine learning * Supervised * Translation
