Smoothness analysis for trajectory features

Dynamic modeling of speech is potentially a major improvement on Hidden Markov Models (HMMs). In one approach, trajectory models[1] are used to model the dynamics of the spectrum, and are used as basis for classi cation [1, 2]. Although some improvement has been achieved in this way, one would hope for more substantial improvements given that the independence assumption is removed. One reason why this was not achieved may be that the trajectory models are based on cepstral coe cients; we show that these tracks contain spurious oscillations. This suggests that these trajectory features might have a high within-class variance. We introduce a measure of evaluating the smoothness of trajectory-based features. This measure provides a method of selecting the best of a set of similar features. Formant trajectories prove to be signi cantly smoother than trajectories of mel scale cepstral coe cients (MFCC) by this measure, but this does not translate directly to improved performance.