Modularity in inductively-learned word pronunciation systems
<|reference_start|>Modularity in inductively-learned word pronunciation systems: In leading morpho-phonological theories and state-of-the-art text-to-speech systems it is assumed that word pronunciation cannot be learned or performed without in-between analyses at several abstraction levels (e.g., morphological, graphemic, phonemic, syllabic, and stress levels). We challenge this assumption for the case of English word pronunciation. Using IGTree, an inductive-learning decision-tree algorithms, we train and test three word-pronunciation systems in which the number of abstraction levels (implemented as sequenced modules) is reduced from five, via three, to one. The latter system, classifying letter strings directly as mapping to phonemes with stress markers, yields significantly better generalisation accuracies than the two multi-module systems. Analyses of empirical results indicate that positive utility effects of sequencing modules are outweighed by cascading errors passed on between modules.<|reference_end|>
@article{bosch1998modularity,
    title={Modularity in inductively-learned word pronunciation systems},
    author={Antal van den Bosch (ILK / Computational Linguistics, Tilburg
  University), Ton Weijters (Dept. of Information Technology, Eindhoven
  University of Technology), and Walter Daelemans (ILK / Computational
  Linguistics, Tilburg University)},
    journal={Proceedings of NeMLaP3/CoNLL98, 185-194},
    year={1998},
    archivePrefix={arXiv},
    eprint={cmp-lg/9801004},
    primaryClass={cmp-lg cs.CL}
}