Adversarial Auto-Encoders Based Model for Classification of Speech Dysarthria

Communication is effective based on various parameters, out of which phonetic or oral communication plays a vital role. Slurred speech or improper speech will lead to misunderstanding in speech, which could toss up any situation. There are many people, ranging from children to adults, who are affected with slurred speech, which is technically termed as Speech Dysarthria, a disease which tampers effective oral communication. Distinguishing between people affected with dysarthria and people with normal speech will be tedious process manually. Machine Learning (ML), and Artificial Intelligence (AI), can be pitched in to solve the problem. There are existing methodologies which classify people affected with speech dysarthria and people who communicate in a normal way. Some of the existing technologies used are Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and so on. This paper aims at distinguishing between people affected with speech dysarthria and people with normal speech, using Adversarial Auto Encoders (AAE), a model which has its roots from Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). This paper brings out a good result and proves to be effective.