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An end-to-end working prototype that detects sign language meanings in images/videos and generate equivalent, realistic voice of words communicated by the sign language, in real-time.

Steven Kolawole

CS Undergrad @FUNAAB, Nigeria

Previously @#3

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Next steps @#3

  • Retrain on the TFOD APIs method using a better architecture
  • Increase dataset’s range to support 50+ sign phrases
  • Support for African languages using Machine Translation
  • Write a paper???

Contact:

kolawolesteven99@gmail.com

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Sign-to-Speech Model for Sign Language Understanding using Object Detection:

A Case Study of Nigerian Sign Language

Steven Kolawole

September 22, 2021

#4

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Synopsis

To develop a lightweight sign-to-speech machine learning application that works in real-time to reduce the communication barrier between the hearing impaired community and the larger society with a focus on sub-Saharan Africa, which is the region with most cases of hearing disabilities.

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Dataset

Creating low-resource dataset with Nigerian Sign Language as a case study;

  • Dataset creation by a sign language TV broadcaster from the Ogun State Broadcasting Corporation (OGBC).
  • Students and teachers from two special education schools in Lagos and Abeokuta, both cities in Nigeria.

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Dataset

Creating low-resource dataset with Nigerian Sign Language as a case study;

  • 8k+ static images of 135+ signs words/phrases with 20+ individuals.
  • Images captured in diverse backgrounds

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Dataset

Image Annotation using LabelImg;

  • Dataset labelled in both YOLO (for YOLO model) and PASCAL VOC (for SSD) format.

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Architecture

  • YOLOv5

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Evaluation

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Evaluation

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Evaluation

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Challenges

  • Annotation was stressful! :(
  • Model cannot capture sequences of spellings
  • Compute resources constraints 😑

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Next steps

  • Finish training SSD model & compare results
  • Finish writing the camera-ready draft
  • Submit to ML4D workshop at NeurIPS
  • Start thinking of graduate school 😅

Contact:

kolawolesteven99@gmail.com

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