Post-Net2.0: An adaptive weighted loss function driven by linguistic constraint for automatic syllable stress detection

Abstract

Automatic syllable stress detection is an essential component in Computer assisted language learning (CALL) systems to guide nonnative language learners. In English, each word typically contains only one primary stressed syllable. However, standard loss functions, such as Binary Cross-Entropy (BCE), often result in predictions where multiple syllables may be stressed or none at all. As a result, automatic syllable stress detection models frequently require an additional post-processing step to ensure that only one syllable is stressed per word. This reliance on post-processing suggests that the model is not fully capturing the stress patterns accurately. To address this issue, we propose an adaptive weighted loss function that builds upon the Stress Intensity Modulation Loss proposed in our recent work of Post-Net. This adaptive weighted loss function is designed to enforce the constraint of a single primary stressed syllable directly during model training. We integrate this loss function into the previously proposed Post-Net (PN_DNN) and on a new architecture which is a hybrid of Post-Net and LSTM (PN_DLSTM). Their performance is compared against the state-of-the-art models trained with standard BCE loss. Experiments conducted on the ISLE corpus reveal that both the models trained only with BCE loss show a significant accuracy gap between with and without post-processing. In contrast, when these models are trained on the proposed adaptive weighted loss function, the gap is narrowed in both the models. Between the two models, the highest reduction is observed in PN_DNN with a decrease from 3.87% to 2.45% & 4.6% to 3.75% for GER & ITA respectively. This indicates that the adaptive weighted loss function effectively captures the linguistic constraint during training, reducing the need for post-processing.