Semi-supervised Learning for Automatic Prosodic Event Detection Using Co-training Algorithm

Most of previous approaches to automatic prosodic event detection are based on supervised learning, relying on the availability of a corpus that is annotated with the prosodic labels of interest in order to train the classification models. However, creating such resources is an expensive and time-consuming task. In this paper, we exploit semi-supervised learning with the co-training algorithm for automatic detection of coarse level representation of prosodic events such as pitch accents, intonational phrase boundaries, and break indices. We propose a confidence-based method to assign labels to unlabeled data and demonstrate improved results using this method compared to the widely used agreement-based method. In addition, we examine various informative sample selection methods. In our experiments on the Boston University radio news corpus, using only a small amount of the labeled data as the initial training set, our proposed labeling method combined with most confidence sample selection can effectively use unlabeled data to improve performance and finally reach performance closer to that of the supervised method using all the training data.