Estimation of relevant timefrequency features using Kendall coefficient for articulator position inference ?
The determination of relevant acoustic information for the inference of articulators position is an open issue. This paper presents a method to estimate those acoustic features better related to articulators movement. The input feature set is based on time-frequency representation calculated from the speech signal, whose parametrization is achieved using the wavelet-packet transform. The main focus is on measuring the relevant acoustic information, in terms of statistical association, for the inference of articulator positions. The rank correlation Kendall coefficient is used as the relevance measure. Attained statistical association is validated using the ?2 ? 2 information measure. The maps of relevant timefrequency features are calculated for the MOCHATIMIT database, where the articulatory information is represented by trajectories of specific positions in the vocal tract. Relevant maps are estimated over the whole speech signal as well as on specific phones, for which a given articulator is known to be critical. The usefulness of the relevant maps is tested in an acoustic-to-articulatory mapping system based on gaussian mixture models.
kendall coefficient wavelet-packet transform articulatory information gmm neural networks