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
Authors

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...