  In this work, we investigate the effectiveness of pretrained Self-Supervised
Learning (SSL) features for learning the mapping for acoustic to articulatory
inversion (AAI). Signal processing-based acoustic features such as MFCCs have
been predominantly used for the AAI task with deep neural networks. With SSL
features working well for various other speech tasks such as speech
recognition, emotion classification, etc., we experiment with its efficacy for
AAI. We train on SSL features with transformer neural networks-based AAI models
of 3 different model complexities and compare its performance with MFCCs in
subject-specific (SS), pooled and fine-tuned (FT) configurations with data from
10 subjects, and evaluate with correlation coefficient (CC) score on the unseen
sentence test set. We find that acoustic feature reconstruction objective-based
SSL features such as TERA and DeCoAR work well for AAI, with SS CCs of these
SSL features reaching close to the best FT CCs of MFCC. We also find the
results consistent across different model sizes.
