Open Access

ARTICLE

Implementation of a Biometric Interface in Voice Controlled Wheelchairs

by Lamia Bouafif, Noureddine Ellouze

1 National Institute of Biomedical Studies of Tunis, 1092, Tunis, Tunisia
2 Image and Signal Processing Laboratory, ENIT BP 37, University of Tunis El Manar, 1064, Tunisia

* Corresponding Author: Noureddine Ellouze. Email:

Sound & Vibration 2020, 54(1), 1-15. https://doi.org/10.32604/sv.2020.08665

Received 22 September 2019; Accepted 26 December 2019; Issue published 01 March 2020

Abstract

In order to assist physically handicapped persons in their movements, we developed an embedded isolated word speech recognition system (ASR) applied to voice control of smart wheelchairs. However, in spite of the existence in the industrial market of several kinds of electric wheelchairs, the problem remains the need to manually control this device by hand via joystick; which limits their use especially by people with severe disabilities. Thus, a significant number of disabled people cannot use a standard electric wheelchair or drive it with difficulty. The proposed solution is to use the voice to control and drive the wheelchair instead of classical joysticks. The intelligent chair is equipped with an obstacle detection system consisting of ultrasonic sensors, a moving navigation algorithm and a speech acquisition and recognition module for voice control embedded in a DSP card. The ASR architecture consists of two main modules. The first one is the speech parameterization module (features extraction) and the second module is the classifier which identifies the speech and generates the control word to motors power unit. The training and recognition phases are based on Hidden Markov Models (HMM), K-means, Baum-Welch and Viterbi algorithms. The database consists of 39 isolated speaker words (13 words pronounced 3 times under different environments and conditions). The simulations are tested under Matlab environment and the real-time implementation is performed by C language with code composer studio embedded in a TMS 320 C6416 DSP kit. The results and experiments obtained gave promising recognition ratio and accuracy around 99% in clean environment. However, the system accuracy decreases considerably in noisy environments, especially for SNR values below 5 dB (in street: 78%, in factory: 52%).

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