RecNorm: Simultaneous Normalisation and Classification applied to Speech Recognition

A particular form of neural network is described, which has terminals for acoustic patterns, class labels and speaker parameters. A method of training this network to "tune in" the speaker parameters to a particular speaker is outlined, based on a trick for converting a supervised network to an unsupervised mode. We describe experiments using this approach in isolated word recognition based on whole-word hidden Markov models. The results indicate an improvement over speaker-independent performance and,for unlabelled data, a performance close to that achieved on labelled data. 1 INTRODUCTION We are concerned to emulate some aspects of perception. In particular, the way that a stimulus which is ambiguous, perhaps because of unknown lighting conditions, can become unambiguous in the context of other such stimuli: the fact that they are subject to tbe same unknown conditions gives our perceptual apparatus enough constraints to solve tbe problem.