Continuous speech input for ASR processing is usually pre-segmented into speech stretches by pauses. In this paper, we propose that smaller, prosodically defined units can be identified by tackling the problem on imbalanced prosodic unit boundary detection using five machine learning techniques. A parsimonious set of linguistically motivated prosodic features has been proven to be useful to characterize prosodic boundary information. Furthermore, BMPM is prone to have true positive rate on the minority class, i.e. the defined prosodic units. As a whole, the decision tree classifier, C4.5, reaches a more stable performance than the other algorithms.
Bibliographic reference. Liu, Yi-Fen / Tseng, Shu-Chuan / Jang, Jyh-Shing Roger / Chen, C.-H. Alvin (2010): "Coping imbalanced prosodic unit boundary detection with linguistically-motivated prosodic features", In INTERSPEECH-2010, 1417-1420.