Abstract
Due to the increased customer demand for various applications in various fields, service robots are becoming very popular. Human Robot Interaction (HRI) is very essential for the good performance of the home service robot. A robot can be controlled by many methods and here the robot is controlled by voice commands which are given orally. The robot must be able to perform various tasks such as speech source localization, source separation and classification etc. The robot must be capable to identify the source accurately. The speech recognizer and a speaker classifier are used to improve the performance of the robot. Reverberation deteriorates the quality and intelligibility of speech. This leads to the poor performance of classification systems. The noise effects can also degrade the performance of the classification systems. The channel may also have ISI. So channel must be equalized to nullify these effects. Here a new blind deconvolutive neural network is used for speech classification in the robot. Using this new method the robot can identify the commands given to it more accurately than by the existing methods.
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