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Enhancing the Efficiency of Voice Controlled Wheelchairs Using NAM for Recognizing Partial Speech in Tamil

机译:使用NAM识别泰米尔语中的部分语音来提高语音控制轮椅的效率

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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.
机译:在本文中,我们提出了一种有效的方法,借助非听觉杂音(NAM)麦克风来识别部分语音,该麦克风对噪声具有鲁棒性。 NAM是一种微弱的杂音,非常微弱,甚至说话者附近的人也听不到。我们可以从人的乳突中识别出这种NAM。只有借助称为NAM?麦克风的特殊类型的麦克风,才能检测到该声音。对于听不到声音但只能说部分单词(半静音)或不完整单词的残障人士,我们可以使用这种方法。我们可以使用NAM麦克风记录和识别部分语音。这种方法可用于解决瘫痪者的问题,他们使用了声控轮椅,可以帮助他们在其他人的帮助下四处走动。目前的语音控制轮椅系统只能识别完全说出的单词,而不能识别半静音或部分讲话受损的人说出的单词。此外,当用于识别来自残障人士的部分语音输入时,它使用具有严重降级和外部噪声影响的普通麦克风。为解决此问题,我们可以将NAM麦克风与泰米尔语语音识别引擎(TSRE)配合使用,以提高结果的准确性。所提出的方法是使用Arduino微控制器套件在类似轮椅的模型中设计和实现的。实验结果表明,该方法可获得80%的准确度,并且证明了与常规麦克风相比,使用NAM麦克风识别部分口语单词的效率更高。

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