Parametric trajectory mixtures for LVCSR

Parametric trajectory models explicitly represent the tem-poral evolution of the speech features as a Gaussian process with time-varying parameters. HMMs are a special case of such models, one in which the trajectory constraints in the speech segment are ignored by the assumption of condi-tional independence across frames within the segment. In this paper, we investigate in detail some extensions to our trajectory modeling approach aimed at improving LVCSR performance: (i) improved modeling of mixtures of trajec-tories via better initialization, (ii) modeling of context de-pendence, and (iii) improved segment boundaries by means of search. We will present results in terms of both phone classi cation and recognition accuracy on the Switchboard corpus. 1