A neural network for classification with incomplete data: application to robust ASR

There are many situations in data classification where the data vector to be classified is partially corrupted, or otherwise incomplete. In this case the optimal estimate for each class probability output, for any given set of missing data components, can be obtained by calculating its expected value. However, this means that classifiers whose expected outputs do not have a closed form expression in terms of the original function parameters, such as the commonly used multi-layer perceptron (MLP), cannot be used for classification with missing data. No classifier can compete with the performance of an MLP on complete data unless it is discriminatively trained. In this paper we present a particular form of RBF classifier which can be discriminatively trained and whose expected outputs are a simple function of the original classifier parameters, even though the output unit function is non-linear. This provides us with an incomplete data classifier network (IDCN) which combines the discriminative classification performance normally associated with artificial neural networks, with the ability to deal gracefully with missing data. We describe two ways in which this IDCN can be applied to robust automatic speech recognition (ASR), depending on whether or not the position of missing data is known. We compare the performance of one of these models with an existing system for ASR with missing data.