Using random forests for prosodic break prediction based on automatic speech labeling

Abstract

In this paper we present a system for automatically predicting prosodic breaks in synthesized speech using the Random Forests classifier. In our experiments the classifier is trained on a large dataset consisting of audiobooks, which is automatically labeled with phone, word, and pause labels. To provide part of speech (POS) tags in the text, a rule-based POS tagger is used. We use crossvalidation in order to be able to examine not only the results for a specific subset of data but also the systems reliability across the dataset. The experimental results demonstrate that the system shows good and consistent results on the audiobook database; the results are poorer and less robust on a smaller database of read speech even though part of that database was labeled manually.