A Ranking Approach to Stress Prediction for Letter-to-Phoneme Conversion

Correct stress placement is important in text-to-speech systems, in terms of both the overall accuracy and the naturalness of pronunciation. In this paper, we formulate stress assignment as a sequence prediction problem. We represent words as sequences of substrings, and use the substrings as features in a Support Vector Machine (SVM) ranker, which is trained to rank possible stress patterns. The ranking approach facilitates inclusion of arbitrary features over both the input sequence and output stress pattern. Our system advances the current state-of-the-art, predicting primary stress in English, German, and Dutch with up to 98% word accuracy on phonemes, and 96% on letters. The system is also highly accurate in predicting secondary stress. Finally, when applied in tandem with an L2P system, it substantially reduces the word error rate when predicting both phonemes and stress.