Diphone multi-trajectory subspace models

Abstract

In this paper we report on the extension of capturing speech transitions embedded in diphones using trajectory models. The slowly varying dynamics of spectral trajectories carry much discriminant information that is very crudely modelled by traditional approaches such as HMMs. We improved our methodology of explicitly capturing the trajectory of short time spectral parameter vectors introducing multi-trajectory concepts in a probabilistic framework. Optimal subspace selection is presented which finds the most discriminant plane for classification. Using the E-set from the TIMIT database results suggest that discriminant information is preserved in the subspace.