Automatic Data-Driven Learning of Articulatory Primitives from Real-Time MRI Data Using Convolutive NMF with Sparseness Constraints
We present a procedure to automatically derive inter-pretable dynamic articulatory primitives in a data-driven manner from image sequences acquired through real-time magnetic resonance imaging (rt-MRI). More specifically, we propose a convolutive Nonnegative Matrix Factorization algorithm with sparseness constraints (cNMFsc) to decompose a given set of image sequences into a set of basis image sequences and an activation matrix. We use a recently-acquired rt-MRI corpus of read speech (460 sentences from 4 speakers) as a test dataset for this procedure. We choose the free parameters of the algorithm empirically by analyzing algorithm performance for different parameter values. We then validate the extracted basis sequences using an articulatory recognition task and finally present an interpretation of the extracted basis set of image sequences in a gesture-based Articulatory Phonology framework.