A NLP-based Approach to Improve Speech Recognition Services for People with Speech Disorders

Current speech recognition services are not suitable for people with speech disorders, which present difficulties in coordinating muscles and articulating words and sentences. In this case, a speaker-dependent approach is strongly required in order to address the specific vocal disarticulation. Several Deep learning approaches have been proposed in the literature to address this problem. However, they require many voice samples of people to properly work, and this is not practical. In this paper, we present an innovative Automatic Speech Recognition (ASR) system which is able to correct failures of deep learning based solution adopting Natural Language Processing (NLP) techniques. The proposed solution can perform both single word and whole sentence corrections by analyzing the speech context. We evaluated the solution in a home automation case study and proved the good accuracy of our model.