Building a Real-Time Sign Language Translator
This article discusses the challenges of building an end-to-end sign language recognition system and the 5-stage pipeline used in the asl-to-voice project to tackle these challenges.
Why it matters
This work demonstrates the potential for AI-powered systems to bridge the communication gap for the deaf and hard-of-hearing communities.
Key Points
- 1Sign language involves complex grammar, facial expressions, and continuous signing without clear word boundaries
- 2The 5-stage pipeline includes keypoint extraction, temporal sequence modeling, gloss decoding, gloss-to-natural language translation, and text-to-speech
- 3The system uses a modular, configuration-driven architecture with technologies like MediaPipe, Transformers, LLMs, and TTS engines
Details
Translating sign language into spoken language in real-time is a complex challenge due to the spatial and temporal nature of sign language, which involves more than just hand shapes. The asl-to-voice project tackles this problem with a 5-stage pipeline. First, 2D and 3D landmarks are extracted from the signer's body using MediaPipe Holistic. Then, a Transformer encoder model learns the temporal relationships in the keypoint sequences. The output probabilities are decoded into a sequence of glosses, which are then translated into natural language using a large language model. Finally, the translated text is converted to speech using a text-to-speech engine. The modular, configuration-driven architecture makes it easy to experiment with different components and technologies.
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