Identifying articulatory goals from kinematic data using principal differential analysis

Articulatory goals can be highly indicative of lexical intentions, but are rarely used in speech classification tasks. In this paper we show that principal differential analysis can be used to learn the behaviours of articulatory motions associated with certain high-level articulatory goals. This method accurately learns the parameters of second-order differential systems applied to data derived by electromagnetic articulography. On average, this approach is between 4.4% and 21.3% more accurate than an HMM and a neural network baseline.

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