Improved speech inversion using general regression neural network

The problem of nonlinear acoustic to articulatory inversion mapping is investigated in the feature space using two models, the deep belief network (DBN) which is the state-of-the-art, and the general regression neural network (GRNN). The task is to estimate a set of articulatory features for improved speech recognition. Experiments with MOCHA-TIMIT and MNGU0 databases reveal that, for speech inversion, GRNN yields a lower root-mean-square error...