COMPUTER-AIDED THERAPY USING AUTOMATIC SPEECH RECOGNITION TECHNIQUE FOR DELAYED LANGUAGE DEVELOPMENT CHILDREN

Abstract

Objectives: This study aims to develop a computer-aided therapy (CAT) application to help children who suffer from delayed language development (DLD) improve their language, especially during the COVID-19 pandemic. Methods: The implemented system teaches the children their body parts using the Egyptian dialect. Two datasets were collected from healthy children (2800 words) and unhealthy children (236 words) who have DLD at the clinic. The model is implemented using a speaker-independent isolated word recognizer based on a discrete-observation hidden Markov model (DHMM) classifier. After the speech signal preprocessing step, K-means algorithm generated a codebook to cluster the speech segments. This task was completed under the MATLAB environment. The graphical user interface was implemented successfully under the C# umbrella to complete the CAT application task. The system was tested on healthy and DLD children. Also, in a small clinical trial, five children who have DLD tested the program in an actual trial to monitor their pronunciation progress during therapeutic sessions. Results: The max recognition rate was 95.25% for the healthy children dataset, while 93.82% for the DLD dataset. Conclusion: DHMM was implemented successfully using nine and five states based on different codebook sizes (160, 200). The implemented system achieved a high recognition rate using both datasets. The children enjoyed using the application because it was interactive. Children who have DLD can use speech recognition applications.