Estimation of stability and accuracy of inverse problem solution for the vocal tract
Speech Communication
Relevance of time-frequency features for phonetic and speaker-channel classification
Speech Communication
Multiresolution signal decomposition: transforms, subands, and wavelets
Multiresolution signal decomposition: transforms, subands, and wavelets
Discriminative wavelet packet filter bank selection for pattern recognition
IEEE Transactions on Signal Processing
An Implementation of Rational Wavelets and Filter Design for Phonetic Classification
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
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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 @g^2 information measure. The maps of relevant time-frequency features are calculated for the MOCHA-TIMIT 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.