  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.
