Paper: Statistical Modeling For Unit Selection In Speech Synthesis

ACL ID P04-1008
Title Statistical Modeling For Unit Selection In Speech Synthesis
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004

Traditional concatenative speech synthesis systems use a number of heuristics to define the target and concatenation costs, essential for the design of the unit selection component. In contrast to these ap- proaches, we introduce a general statistical model- ing framework for unit selection inspired by auto- matic speech recognition. Given appropriate data, techniques based on that framework can result in a more accurate unit selection, thereby improving the general quality of a speech synthesizer. They can also lead to a more modular and a substantially more efficient system. We present a new unit selection system based on statistical modeling. To overcome the original ab- sence of data, we use an existing high-quality unit selection system to generate a corpus of unit se- quences. We sho...