Facial Movement Recognition Using CNN-BiLSTM in Vowel for Bahasa Indonesia

Abstract

Speaking is a multimodal phenomenon that has both verbal and non-verbal cues. One of the non-verbal cues in speaking is the facial movement of the subject, which can be used to find the letter being spoken by the subject. Previous research has been done to prove that lip movement can translate to vowels for Bahasa Indonesia, but detecting the whole facial movement is yet to be covered. This research aimed to establish a CNN-BiLSTM model that can learn spoken vowels by reading the subject's facial movements. The CNN-BiLSTM model yielded a 98.66% validation accuracy, with over 94% accuracy for all five vowels. The model is also capable of recognizing whether the subject is currently silent or speaking a vowel with 98.07% accuracy.