Sequence-to-Sequence Articulatory Inversion Through Time Convolution of Sub-Band Frequency Signals

We propose a new acoustic-to-articulatory inversion (AAI) sequence-to-sequence neural architecture, where spectral sub-bands are independently processed in time by 1-dimensional (1-D) convolutional filters of different sizes. The learned features maps are then combined and processed by a recurrent block with bi-directional long short-term memory (BLSTM) gates for preserving the smoothly varying nature of the articulatory trajectories. Our experimental evidence shows that, on a speaker dependent AAI task, in spite of the reduced number of parameters, our model demonstrates better root mean squared error (RMSE) and Pearson's correlation coefficient (PCC) than a both a BLSTM model and an FC-BLSTM model where the first stages are fully connected layers. In particular, the average RMSE goes from 1.401 when feeding the filterbank features directly into the BLSTM, to 1.328 with the FC-BLSTM model, and to 1.216 with the proposed method. Similarly, the average PCC increases from 0.859 to 0.877, and 0.895, respectively. On a speaker independent AAI task, we show that our convolutional features outperform the original filterbank features, and can be combined with phonetic features bringing independent information to the solution of the problem. To the best of the authors' knowledge, we report the best results on the given task and data.