We propose an information theoretic region selection algorithm from the real time magnetic resonance imaging (rtMRI) video frames for a broad phonetic class recognition task. Representations derived from these optimal regions are used as the articulatory features for recognition. A set of connected and arbitrary shaped regions are selected such that the articulatory features computed from such regions provide maximal information about the broad phonetic classes. We also propose a tree-structured greedy region splitting algorithm to further segment these regions so that articulatory features from these split regions enhance the information about the phonetic classes. We find that some of the proposed articulatory features correlate well with the articulatory gestures from the Articulatory Phonology theory of speech production. Broad phonetic class recognition experiment using four rtMRI subjects reveals that the recognition accuracy with optimal split regions is, on average, higher than that using only acoustic features. Combining acoustic and articulatory features further reduces the error-rate by ∼8.25% (relative). 
Information theoretic optimal vocal tract region selection from real time magnetic resonance images for broad phonetic class recognition