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.

翻译：深度学习模型的性能在很大程度上取决于其高效编码输入特征并将其解码为有意义输出的能力。更优的输入和输出表示有望提升模型的性能与泛化能力。在声学-发音语音反演系统研究中，我们探究了利用自监督学习模型（如HuBERT）获取的语音表示相较于传统声学特征的影响。此外，我们通过改进的几何变换模型研究了新型声道变量的引入。通过结合这两种方法，我们将评估语音反演系统中声道变量估计精度的皮尔逊积矩相关系数从0.7452提升至0.8141，增幅达6.9%。我们的研究结果强调了自监督学习模型丰富的特征表示以及与目标声道变量相关的改进几何变换对增强语音反演系统功能的深远影响。