  Reconstruction of articulatory trajectories from the acoustic speech signal
has been proposed for improving speech recognition and text-to-speech
synthesis. However, to be useful in these settings, articulatory reconstruction
must be speaker independent. Furthermore, as most research focuses on single,
small datasets with few speakers, robust articulatory reconstrucion could
profit from combining datasets. Standard evaluation measures such as root mean
square error and Pearson correlation are inappropriate for evaluating the
speaker-independence of models or the usefulness of combining datasets. We
present a new evaluation for articulatory reconstruction which is independent
of the articulatory data set used for training: the phone discrimination ABX
task. We use the ABX measure to evaluate a Bi-LSTM based model trained on 3
datasets (14 speakers), and show that it gives information complementary to the
standard measures, and enables us to evaluate the effects of dataset merging,
as well as the speaker independence of the model.
