  Text-to-speech conversion has traditionally been performed either by
concatenating short samples of speech or by using rule-based systems to convert
a phonetic representation of speech into an acoustic representation, which is
then converted into speech. This paper describes a system that uses a
time-delay neural network (TDNN) to perform this phonetic-to-acoustic mapping,
with another neural network to control the timing of the generated speech. The
neural network system requires less memory than a concatenation system, and
performed well in tests comparing it to commercial systems using other
technologies.
