$ $Acoustic-to-articulatory inversion (AAI) involves mapping from the
acoustic space to the articulatory space. Signal-processing features like the
MFCCs, have been widely used for the AAI task. For subjects with dysarthric
speech, AAI is challenging because of an imprecise and indistinct
pronunciation. In this work, we perform AAI for dysarthric speech using
representations from pre-trained self-supervised learning (SSL) models. We
demonstrate the impact of different pre-trained features on this challenging
AAI task, at low-resource conditions. In addition, we also condition x-vectors
to the extracted SSL features to train a BLSTM network. In the seen case, we
experiment with three AAI training schemes (subject-specific, pooled, and
fine-tuned). The results, consistent across training schemes, reveal that
DeCoAR, in the fine-tuned scheme, achieves a relative improvement of the
Pearson Correlation Coefficient (CC) by ${\sim}$1.81\% and ${\sim}$4.56\% for
healthy controls and patients, respectively, over MFCCs. In the unseen case, we
observe similar average trends for different SSL features. Overall, SSL
networks like wav2vec, APC, and DeCoAR, which are trained with feature
reconstruction or future timestep prediction tasks, perform well in predicting
dysarthric articulatory trajectories.

使用预先训练的自监督学习模型，对发音不清的言语进行声学到发音学的逆向映射，通过条件化 x-vectors 来训练 BLSTM 网络，使用不同的预先训练特征进行低资源条件下的挑战性声学到发音学逆向映射任务，在实验中观察到 DeCoAR 在细调方案中相对于 MFCC 的皮尔森相关系数分别在健康控制组和患者组上提高了约 1.81% 和约 4.56%，同时发现具有特征重构或未来时间步预测任务的 SSL 网络（如 wav2vec、APC 和 DeCoAR）预测发音不清的发声轨迹的性能表现良好。