Improving prosodic break detection in a Russian TTS system

Abstract

We propose using statistical methods for predicting positions and durations of prosodic breaks in a Russian TTS system, in order to improve on a baseline rule-based system. The paper reports experiments with CART and Random Forests (RF) classifiers. We used CART to predict break durations inside and between sentences, and compared the results of CART and RF for predicting break positions inside sentences. We find that both classifiers show an improvement over the baseline system in predicting break positions, with RF showing the best results. We also observe good results in experiments with predicting break durations. To increase the naturalness of synthesized speech, we included probability-based break durations into a working Russian TTS system. We also built an experimental system with probability-based break placement in sentence parts without punctuation marks, which was evaluated higher than the baseline system in a pilot listening experiment. © 2013 Springer International Publishing.