UC Berkeley

This dissertation proposes a method for the automatic design and extraction of prosodic features. The system trains a heteroscedastic linear discriminant analysis (HLDA) transform using supervised learning on sentence boundary labels plus feature selection to create a set of discriminant features for sentence segmentation. To my knowledge, this is the first attempt to automatically design prosodic features. The motivation for automatic feature design is to employ machine learning techniques in aid of a task that hitherto places heavy reliance on time-intensive experimentation by a researcher with in-domain expertise. Previous prosodic feature sets have tended to be manually optimized for a particular language, so that, for instance, features developed for English are comparatively ineffective for Mandarin. While unsurprising, this suggests that an automatic approach to learning good features for a new language may be of assistance. The proposed method is tested in English and Mandarin to determine whether it can adjust to the idiosyncracies of different languages. This study finds that, by being able to draw on more contextual information, the HLDA system can perform about as well as the baseline features.