Open Access

ARTICLE

Speech Recognition-Based Automated Visual Acuity Testing with Adaptive Mel Filter Bank

Shibli Nisar¹, Muhammad Asghar Khan^2,*, Fahad Algarni³, Abdul Wakeel¹, M. Irfan Uddin⁴, Insaf Ullah²
1 Department of Electrical Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan
2 Hamdard Institute of Engineering & Technology, Islamabad, 44000, Pakistan
3 College of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia
4 Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan
* Corresponding Author: Muhammad Asghar Khan. Email:
(This article belongs to this Special Issue: Recent Advances in Metaheuristic Techniques and Their Real-World Applications)

Computers, Materials & Continua 2022, 70(2), 2991-3004. https://doi.org/10.32604/cmc.2022.020376

Received 21 May 2021; Accepted 23 June 2021; Issue published 27 September 2021

Abstract

One of the most commonly reported disabilities is vision loss, which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient. This procedure, however, usually requires an appointment with an ophthalmologist, which is both time-consuming and expensive process. Other issues that can arise include a lack of appropriate equipment and trained practitioners, especially in rural areas. Centered on a cognitively motivated attribute extraction and speech recognition approach, this paper proposes a novel idea that immediately determines the eyesight deficiency. The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for feature extraction. The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment. Comparative performance evaluation demonstrates the potential of our automated visual acuity test method to achieve comparable results to the clinical ground truth, established by the expert ophthalmologist’s tests. The overall accuracy achieved by the proposed model when compared with the expert ophthalmologist test is 91.875%. The proposed method potentially offers a second opinion to ophthalmologists, and serves as a cost-effective pre-screening test to predict eyesight loss at an early stage.

---

Keywords

Eyesight test; speech recognition; HMM; SVM; feature extraction

---