Logo Lanfrica

dhiaabdelli12/tunisian-sign-language-seq-classification-dataset-building

Domaine:

natural language processing

Type de record:

dataset
Créateur:
dhi
Hôte:
In this phase, we attempt to build a dictionary for the tunisian sign language and gather datasets for our models # Building The Dataset In this phase, we attempt to build a dictionary for the tunisian sign language and gather datasets for our models. ## Context As a first step, we have decided to limit ourselves to the context of a medical consultation. We've worked with a general practitioner that described the medical consultation. And using that description, we've extracted a list of words that we will use for our dictionary. ## Building from Scratch We've then sent the words to Tunisian Sign Language interpreters working with ATILS (Association Tunisienne des Interprètes en Langues des Signes) whom we've reached through the president. These interpreters sent us back the sign for each term we've picked. ## Already Available Ressources In addition, we've used the "Medical Dictionary in Tunisian Sign Languages" which was made by AVST (Association Voix du Sourd Tunisienne) in cooperation with the French Institute. This booklet contains 160 words used in the context of a medical consultation with their respective signs. Through the help of our doctor, we've sampled 60 relevant words as a starting size for our vocabulary (we will be going back to add the rest of the words once we have a minimal viable product: a working classifier model with a certain accuracy threshold to be determined). The extracted words can be found in the words.txt file. ## Automation We've used a python script to generate a folder for each word using Google Drive's API. We've divided the work among ourselves so that each member has 10 words. Each member shoots a video (of themselves and other people) mimicking the signs for each of their words and then uploads them to their respective folder on drive. ## Video shooting guidlines These are the rules we imposed when we shot the videos: - FPS (frames per second): 30 - Min/Max quality: 720p - Framing: follow "Medical Dictionary in Tunisian Sign Languages" picture framing - Sign speed: calibrate to one of the interpreters' signing speed - Orientation: …