  Acoustic-to-articulatory inversion (AAI) is to obtain the movement of
articulators from speech signals. Until now, achieving a speaker-independent
AAI remains a challenge given the limited data. Besides, most current works
only use audio speech as input, causing an inevitable performance bottleneck.
To solve these problems, firstly, we pre-train a speech decomposition network
to decompose audio speech into speaker embedding and content embedding as the
new personalized speech features to adapt to the speaker-independent case.
Secondly, to further improve the AAI, we propose a novel auxiliary feature
network to estimate the lip auxiliary features from the above personalized
speech features. Experimental results on three public datasets show that,
compared with the state-of-the-art only using the audio speech feature, the
proposed method reduces the average RMSE by 0.25 and increases the average
correlation coefficient by 2.0% in the speaker-dependent case. More
importantly, the average RMSE decreases by 0.29 and the average correlation
coefficient increases by 5.0% in the speaker-independent case.
