Author(s)

Angappan Kumaresan^1,2*, Nagarajan Mohankumar³, Mathavan Sureshanand⁴, Jothi Suganya⁵

¹Anna University, Chennai, India.
²Department of Computer Science and Engineering, SKP Engineering College, Tiruvannamalai, India.
³Department of Electronics and Communication Engineering, SKP Engineering College, Tiruvannamalai, India.
⁴Department of Computer Science and Engineering, Sri Sairam Engineering College, Chennai, India.
⁵Department of Electronics and Communication Engineering, Mailam Engineering College, Villupuram, India.

In this paper, we have presented an effective method for recognizing partial speech with the help of Non Audible Murmur (NAM) microphone which is robust against noise. NAM is a kind of soft murmur that is so weak that even people nearby the speaker cannot hear it. We can recognize this NAM from the mastoid of humans. It can be detected only with the help of a special type of microphone termed as NAM microphone. We can use this approach for impaired people who can hear sound but can speak only partial words (semi-mute) or incomplete words. We can record and recognize partial speech using NAM microphone. This approach can be used to solve problems for paralysed people who use voice controlled wheelchair which helps them to move around without the help of others. The present voice controlled wheelchair systems can recognize only fully spoken words and can’t recognise words spoken by semi-mute or partially speech impaired people. Further it uses normal microphone which hassevere degradation and external noise influence when used for recognizing partial speech inputs from impaired people. To overcome this problem, we can use NAM microphone along with Tamil Speech Recognition Engine (TSRE) to improve the accuracy of the results. The proposed method was designed and implemented in a wheelchair like model using Arduino microcontroller kit. Experimental results have shown that 80% accuracy can be obtained in this method and also proved that recognizing partially spoken words using NAM microphone was much efficient compared to the normal microphone.

NAM, Speech Recognition, TSRE, Wheelchair Guidance, HCI

Kumaresan, A. , Mohankumar, N. , Sureshanand, M. and Suganya, J. (2016) Enhancing the Efficiency of Voice Controlled Wheelchairs Using NAM for Recognizing Partial Speech in Tamil. Circuits and Systems, 7, 2884-2892. doi: 10.4236/cs.2016.710247.