科研之友
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摘要
Most 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 accent, intonational phrase boundaries, and break indices. Since co-training works on the condition that the views are compatible and uncorrelated, and real data often do not satisfy these conditions, we propose a method to label and select examples in co-training. In our experiments on the Boston University radio news corpus, when using only a small amount of the labeled data as the initial training set, our proposed labeling method can effectively use unlabeled data to improve performance and finally reach performance close to the results of the supervised method using more labeled data. We perform a thorough analysis of various factors impacting the learning curves, including labeling error rate and informativeness of added examples, performance of the individual classifiers and their difference, and the initial and added data size.
- 出版日期2012-3
