Articulatory Data of Audiovisual Records of Speech Connected by Machine Learning
Réka Trencsényi, László Czap · 2022
The center of attraction of the present study is the application of neural networks for combining data arising from dynamic audiovisual sources made by ultrasound and magnetic resonance imaging methods, which store image and sound signals recorded during human speech. The objectives of machine learning are tongue contours fitted to the frames of the audiovisual packages by automatic contour tracking algorithms.