Abstract
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.
Highlights
The ordinary human speech, which is conducted through air, is produced by the vocal cord vibration due to the air stream from lungs
The partial speech recorded using Non Audible Murmur (NAM) microphone has been applied in voice controlled wheelchairs even for people who can speak partially and a compromising level of accuracy is reached
We have proposed and used NAM microphone for recognizing partial speech in paralysed people and integrated that into a wheelchair guidance system using TSRE (Tamil Speech Recognition Engine)
Summary
The ordinary human speech, which is conducted through air, is produced by the vocal cord vibration due to the air stream from lungs. The above specified sound passes through the tissues and bones which can be sensed and recognized using body conduction microphone This is a soft murmur which cannot be heard by people around [1]. The author has implemented the general adaptation algorithm within the CNET speech recognition system that was evaluated on several telephone databases and using these methodologies, a systematic convergence was achieved in an online unsupervised mode. In this unified approach, considering N1(., μ1. A robust speech recognition method based on stochastic mapping technique was proposed by Mohammed Afify et al, in which a Gaussian mixture model was constructed for joint distribution of noisy and clean features. This system is found to be more effective in terms of performance and cost without any need of complex sensors or artificial intelligence decisions [8]
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