Preliminary inversion mapping results with a new EMA corpus

In this paper, we apply our inversion mapping method, the trajectory mixture density network (TMDN), to a new corpus of articulatory data, recorded with a Carstens AG500 electromagnetic articulograph. This new data set, mngu0, is relatively large and phonetically rich, among other beneficial characteristics. We obtain good results, with a root mean square (RMS) error of only 0.99mm. This compares very well with our previous lowest result of 1.54mm RMS error for equivalent coils of the MOCHA fsew0 EMA data. We interpret this as showing the mngu0 data set is potentially more consistent than the fsew0 data set, and is very useful for research which calls for articulatory trajectory data. It also supports our view that the TMDN is very much suited to the inversion mapping problem. Index Terms: acoustic-articulatory, inversion mapping, neural network.

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