  We describe the approach to linguistic variation taken by the Motorola speech
synthesizer. A pan-dialectal pronunciation dictionary is described, which
serves as the training data for a neural network based letter-to-sound
converter. Subsequent to dictionary retrieval or letter-to-sound generation,
pronunciations are submitted a neural network based postlexical module. The
postlexical module has been trained on aligned dictionary pronunciations and
hand-labeled narrow phonetic transcriptions. This architecture permits the
learning of individual postlexical variation, and can be retrained for each
speaker whose voice is being modeled for synthesis. Learning variation in this
way can result in greater naturalness for the synthetic speech that is produced
by the system.
