Parametric subspace modeling of speech transitions

Abstract This paper describes an attempt at capturing segmental transition information for speech recognition tasks. The slowly varying dynamics of spectral trajectories carries much discriminant information that is very crudely modelled by traditional approaches such as HMMs. In approaches such as recurrent neural networks there is the hope, but not the convincing demonstration, that such transitional information could be captured. The method presented here starts from the very different position of explicitly capturing the trajectory of short time spectral parameter vectors on a subspace in which the temporal sequence information is preserved. This was approached by introducing a temporal constraint into the well known technique of Principal Component Analysis (PCA). On this subspace, an attempt of parametric modelling the trajectory was made, and a distance metric was computed to perform classification of diphones. Using the Principal Curves method of Hastie and Stuetzle and the Generative Topographic map (GTM) technique of Bishop, Svensen and Williams as description of the temporal evolution in terms of latent variables was performed. On the difficult problem of /bee/, /dee/, /gee/ it was possible to retain discriminatory information with a small number of parameters. Experimental illustrations present results on ISOLET and TIMIT database.