We introduce Whistler, a trainable Text-to-Speech (TTS)
system, that automatically learns the model parameters from a
corpus. Both prosody parameters and concatenative speech units
are derived through the use of probabilistic learning methods
that have been successfully used for speech recognition. Whistler
can produce synthetic speech that sounds very natural and
resembles the acoustic and prosodic characteristics of the
original speaker. The underlying technologies used in Whistler
can significantly facilitate the process of creating generic TTS
systems for a new language, a new voice, or a new speech style.