Building a Sign-to-Speech Prototype with TensorFlow, Pytorch, and DeepStack
summary
End-to-end prototype converts live sign video to spoken words using TensorFlow, PyTorch and DeepStack, with a Nigerian Sign Language dataset (≈8,000 images, 5,000 after cleaning) from 20+ participants and diverse lighting conditions. Models tested include YOLOv5 and SSD for detection and MobileNetV2 classifier (fine‑tuned ~52 layers, trained ~150 epochs), achieving mAP@0.5 ≈ 0.95; output routes into a TTS engine. Major burdens are manual annotation, consent/privacy for faces, and constrained compute (Colab/ML Collective). Real‑time sign→speech pipeline (object detection → classifier → TTS).
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chapters (13)
- 0:00Introduction and speaker intro
- 1:03Project overview and motivation
- 3:38Project background and challenges
- 8:18Data collection and dataset creation
- 11:51Data annotation and preprocessing
- 13:18Model experiments and training
- 15:11Results and model evaluation
- 17:07Final model selection and tuning
- 19:18Deployment and demo
- 21:01Lessons learned and challenges
- 24:56Career insights and advice
- 27:45Balancing impact and personal growth
- 29:08Resources and closing
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