ISCA Archive ICSLP 2000
ISCA Archive ICSLP 2000

Automatic methods for lexical stress assignment and syllabification

Steve Pearson, Roland Kuhn, Steven Fincke, Nick Kibre

Improvements in automatic lexical stress assignment and syllabification can increase the quality of text-to-speech synthesis as well as decrease the memory requirements for dictionaries. Several methods were evaluated. Machine-learning based methods are preferred since they easily adapt to multiple languages. For stress prediction, encouraging results were obtain by combining a decision tree approach with an algorithm that uses global (word level) statistical data derived from the training dictionary. For syllable boundary prediction, algorithms that learn syllable level statistics from the training dictionary perform very well, and can be implemented as a post-process after prediction of phoneme transcription and stress.


Cite as: Pearson, S., Kuhn, R., Fincke, S., Kibre, N. (2000) Automatic methods for lexical stress assignment and syllabification. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 2, 423-426

@inproceedings{pearson00_icslp,
  author={Steve Pearson and Roland Kuhn and Steven Fincke and Nick Kibre},
  title={{Automatic methods for lexical stress assignment and syllabification}},
  year=2000,
  booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
  pages={vol. 2, 423-426}
}