Handicapped Wheelchair Movements Using Discrete Arabic Command Recognition

(Khalid M.O. Nahar, Moyawiah al-shannaq, Rafat Alshorman, Ra’ed M. Al-Khatib, Mohammad Ashraf Ot.tom)

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Abstract

Automatic Speech Recognition (ASR) is an effective and widespread method used to interpret the human voice into commands. Over the past few decades and due to the great evolution in data science, the processing of speech has been embedded in various new technologies. The benefits of such evolution motivated us to develop a discrete voice system for controlling the wheelchair movements for Arab people with physical impairment (handicapped people). The wheelchair via voice system was able to recognize seven isolated words in the Arabic language. The approach uses the CMU-Sphinx4 ASR for evaluating the effectiveness and the accuracy of the recognition system. A corpus was built, and then the system was trained on 70% of this corpus and tested using the rest of 30%. The experiments reveal that the recognition rate of the proposed approach is 96%. The average level of accuracy of the real experiment and corpus-based testing experiment reached 94%. Keywords: Acoustic Model, Arabic Speech Recognition, Handicapped, Hidden Markov Model, Language Model, Wheelchair.
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