Understanding speech recognition using correlation-generated neural network targets

Training neural networks with variable targets for speech recognition systems has been shown to be effective in improving word accuracy. In this correspondence, a new and simple method for estimating variable targets for a given training pattern is presented. It uses estimated correlations between different output nodes of a neural network to create a set of variable targets for each training pattern. Experimental results show that the word error is reduced by more than 20% when these new correlation-based targets are compared to more conventional zero/one targets with a squared-error cost function. Performance with these new targets approaches that of high-performance hidden Markov model (HMM) recognizers but requires far fewer parameters.