  Data augmentation has proven to be a promising prospect in improving the
performance of deep learning models by adding variability to training data. In
previous work with developing a noise robust acoustic-to-articulatory speech
inversion system, we have shown the importance of noise augmentation to improve
the performance of speech inversion in noisy speech. In this work, we compare
and contrast different ways of doing data augmentation and show how this
technique improves the performance of articulatory speech inversion not only on
noisy speech, but also on clean speech data. We also propose a Bidirectional
Gated Recurrent Neural Network as the speech inversion system instead of the
previously used feed forward neural network. The inversion system uses
mel-frequency cepstral coefficients (MFCCs) as the input acoustic features and
six vocal tract-variables (TVs) as the output articulatory features. The
Performance of the system was measured by computing the correlation between
estimated and actual TVs on the U. Wisc. X-ray Microbeam database. The proposed
speech inversion system shows a 5% relative improvement in correlation over the
baseline noise robust system for clean speech data. The pre-trained model, when
adapted to each unseen speaker in the test set, improves the average
correlation by another 6%.
