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

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 118–126, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP A Ranking Approach to Stress Prediction for Letter-to-Phoneme Conversion Qing Dou, Shane Bergsma, Sittichai Jiampojamarn and Grzegorz Kondrak Department of Computing Science University of Alberta Edmonton, AB, T6G 2E8, Canada {qdou,bergsma,sj,kondrak}@cs.ualberta.ca Abstract 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 formu- late stress assignment as a sequence pre- diction problem. We represent words as sequences of substrings, and use the sub- strings as features in a Support Vector Ma- chine (SVM) ranker, which is trained to rank possible stress patterns. The rank- ing approach facilitates inclusion of arbi- trary features over both the input sequence and output stress pattern. Our system ad- vances the current state-of-the-art, predict- ing primary stress in English, German, and Dutch with up to 98% word accuracy on phonemes, and 96% on letters. The sys- tem is also highly accurate in predicting secondary stress. Finally, when applied in tandem with an L2P system, it substan- tially reduces the word error rate when predicting both phonemes and stress. 1 Introduction In many languages, certain syllables in words are phonetically more prominent in terms of duration, pitch, and loudness. This phenomenon is referred to as lexical stress. In some languages, the loca- tion of stress is entirely predictable. For example, lexical stress regularly falls on the initial syllable in Hungarian, and on the penultimate syllable in Polish. In other languages, such as English and Russian, any syllable in the word can be stressed. Correct stress placement is important in text- to-speech systems because it affects the accuracy of human word recognition (Tagliapietra and Ta- bossi, 2005; Arciuli and Cupples, 2006). How- ever, the issue has often been ignored in previ- ous letter-to-phoneme (L2P) systems. The sys- tems that do generate stress markers often do not report separate figures on stress prediction accu- racy, or they only provide results on a single lan- guage. Some only predict primary stress mark- ers (Black et al., 1998; Webster, 2004; Demberg et al., 2007), while those that predict both primary and secondary stress generally achieve lower ac- curacy (Bagshaw, 1998; Coleman, 2000; Pearson et al., 2000). In this paper, we formulate stress assignment as a sequence prediction problem. We divide each word into a sequence of substrings, and use these substrings as features for a Support Vector Ma- chine (SVM) ranker. For a given sequence length, there is typically only a small number of stress patterns in use. The task of the SVM is to rank the true stress pattern above the small number of acceptable alternatives. This is the first system to predict stress within a powerful discriminative learning framework. By using a ranking approach, we enable the use of arbitrary features over the en- tire (input) sequence and (output) stress pattern. We show that the addition of …

Content truncated to 500 words