A wavelet- and neural network-based voice interface system for wheelchair control

Voice control has long been considered as a natural mechanism to assist powered wheelchair users. However, one implementation difficulty is that a voice input system may fail to recognise a user’s voice. Indeed, speech activated interface between human and autonomous/semi-autonomous systems requires accurate detection and recognition. In this area pitch and end-point detection is of vital importance. This paper presents a new method for pitch detection based on the continuous wavelet transform phase. The proposed technique can serve as an accurate pitch detector, and also can offer an efficient solution to the end-point detection problem. The extracted features from a user’s speech are then used to train a neural network for speech recognition. Experimental results are provided for the detection of pitch periods and end points and the recognition of a number of commands of male and female users. Laboratory tests are reported for the proposed voice control wheelchair system.