Optimizing Automatic Speech Assessment: W-RankSim Regularization and Hybrid Feature Fusion Strategies
This work addresses data imbalance issues in ASA for English test datasets, offering incremental improvements through novel regularization and feature combination.
The paper tackled the problem of imbalanced data in Automatic Speech Assessment (ASA) by introducing W-RankSim regularization for ordinal classification and a hybrid feature fusion model, resulting in improved performance as confirmed by experimental evaluations.
Automatic Speech Assessment (ASA) has seen notable advancements with the utilization of self-supervised features (SSL) in recent research. However, a key challenge in ASA lies in the imbalanced distribution of data, particularly evident in English test datasets. To address this challenge, we approach ASA as an ordinal classification task, introducing Weighted Vectors Ranking Similarity (W-RankSim) as a novel regularization technique. W-RankSim encourages closer proximity of weighted vectors in the output layer for similar classes, implying that feature vectors with similar labels would be gradually nudged closer to each other as they converge towards corresponding weighted vectors. Extensive experimental evaluations confirm the effectiveness of our approach in improving ordinal classification performance for ASA. Furthermore, we propose a hybrid model that combines SSL and handcrafted features, showcasing how the inclusion of handcrafted features enhances performance in an ASA system.