A Neural Network for Classification with Incomplete Data

If the data vector for input to an automatic classifier is incomplete, the optimal estimate for each class probability must be calculated as the expected value of the classifier output. We identify a form of Radial Basis Function (RBF) classifier whose expected outputs can easily be evaluated in terms of the original function parameters. Two ways are described in which this classifier can be applied to robust automatic speech recognition, depending on whether or not the position of missing data is known. Acknowledgements: This work was supported by the EC/OFES (European Community / Swiss Federal Office for Education and Science) RESPITE project (REcognition of Speech by Partial Information TEchniques). Recognition tests for the methods presented in this report were carried out in collaboration with the speech group at Sheffield University, U.K.