The performance of deep learning models depends significantly on their
capacity to encode input features efficiently and decode them into meaningful
outputs. Better input and output representation has the potential to boost
models' performance and generalization. In the context of
acoustic-to-articulatory speech inversion (SI) systems, we study the impact of
utilizing speech representations acquired via self-supervised learning (SSL)
models, such as HuBERT compared to conventional acoustic features.
Additionally, we investigate the incorporation of novel tract variables (TVs)
through an improved geometric transformation model. By combining these two
approaches, we improve the Pearson product-moment correlation (PPMC) scores
which evaluate the accuracy of TV estimation of the SI system from 0.7452 to
0.8141, a 6.9% increase. Our findings underscore the profound influence of rich
feature representations from SSL models and improved geometric transformations
with target TVs on the enhanced functionality of SI systems.

通过结合利用自我监督学习模型和改进的几何变换模型，我们提高了从 0.7452 到 0.8141 的皮尔逊积矩相关系数（PPMC）得分，从而增加了 6.9％，从而突出了来自自我监督学习模型和改进的几何变换模型对语音反演系统功能的重大影响。