Parametrisation of the speech space using the self-organising neural network

Speech recognition is a diicult problem due to the inability of current systems to cope with connected speech. Neural networks are able to learn some aspects of this task. An unsupervised learning scheme like the self-organising map can be used to both classify and order the speech sounds and provide a front end to higher level processing. A map of phonemes (phonotopic map) is used to trace trajectories of sounds from utterances. The self-organising map provides a means of reducing the inherent dimensionality of the speech data. A crinkle factor which is used to determine how close the dimensionality of the map is to the dimensionality of the speech input shows that speech has an inherent dimensionality of at least three or four. A projection of the map and the speech data shows how the self-organising map ts the speech space.