We use mutual information as the criterion to rank the Mel frequency cepstral coefficients (MFCCs) and their derivatives according to the information they provide about different articulatory features in acoustic-to-articulatory (AtoA) inversion. It is found that just a small subset of the coefficients encodes maximal information about articulatory features and interestingly, this subset is articulatory feature specific. We use these subsets of MFCCs(+derivatives) in AtoA inversion using Gaussian mixture model (GMM) mapping. Inversion experiments with articulatory data support the information theoretic finding that the subsets of MFCCs(+derivatives) as selected by feature ranking method are sufficient to achieve an inversion performance similar to that obtained by a conventional full set of MFCCs(+derivatives). This drastically reduces the modeling complexity of the acoustic-articulatory map using GMM without degrading inversion performance significantly.
Bibliographic reference. Ghosh, Prasanta Kumar / Narayanan, Shrikanth (2013): "Information theoretic acoustic feature selection for acoustic-to-articulatory inversion", In INTERSPEECH-2013, 3177-3181.