Open Access
ARTICLE
Implementation of a Biometric Interface in Voice Controlled Wheelchairs
Lamia Bouafif1, Noureddine Ellouze2,*
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%).
Keywords
Cite This Article
Bouafif, L., Ellouze, N. (2020). Implementation of a Biometric Interface in Voice Controlled Wheelchairs.
Sound & Vibration, 54(1), 1–15. https://doi.org/10.32604/sv.2020.08665