Automatic syllable stress detection under non-parallel label and data condition

Abstract

Typically, automatic syllable stress detection is posed as a supervised classification problem, for which, a classifier is trained using manually annotated (existing) syllable data and stress labels. However, in real testing scenarios, syllable data is estimated since manual annotation is not possible. Further, the estimation process could result in a mismatch between the lengths of the estimated and the existing syllable data causing no one-to-one correspondence between the estimated syllable data and the existing labels. Hence, the existing labels and estimated syllable data together cannot be used to train the classifier. This can be avoided by manually labeling the estimated syllable data, which, however, is impractical. In contrast, we, in this work, propose a method to obtain labels for estimated syllable data without using manual annotation. The proposed method considers a weighted version of the well-known Wagner–Fisher algorithm (WFA) to assign the existing labels to the estimated syllable data, where the weights are computed based on a set of three constraints defined in the proposed algorithm. Experiments on ISLE corpus show that the performance obtained on the test set for four different types of estimated syllable data are higher when the assigned labels and estimated syllable data are used for training compared to those when existing labels and existing syllable data are used. Also, the label assignment accuracy using the proposed method is found to be higher than that using a baseline scheme based on WFA.