Paper: Re-Engineering Letter-To-Sound Rules

ACL ID N01-1015
Title Re-Engineering Letter-To-Sound Rules
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2001
Authors

Using finite-state automata for the text analysis component in a text-to-speech system is problem- atic in several respects: the rewrite rules from which the automata are compiled are difficult to write and maintain, and the resulting automata can become very large and therefore inefficient. Converting the knowledge represented explicitly in rewrite rules into a more efficient format is difficult. We take an indirect route, learning an efficient decision tree rep- resentation from data and tapping information con- tained in existing rewrite rules, which increases per- formance compared to learning exclusively from a pronunciation lexicon.