Scalable implementation of unit selection based text-to-speech system for embedded solutions

In this paper we propose two methods in order to implement unit selection-based text-to-speech engine into resource-limited embedded systems. While we have achieved improving the quality of synthesized speech by unit selection-based text-to-speech technology, there is a practical problem regarding the trade-off between the size of database and the quality of synthesized speech. That is, we need large database and expensive computation in order to generate highly natural sounding voices, and the text-to-speech system is required to meet the specification of target system. For this problem, we introduced frequency-based approaches to reduce the size of speech database. The experimental results showed the step-by-step downsizing method was better than the direct one in terms of the cumulative join cost and the target cost. Furthermore, some techniques were introduced and evaluated in order to implement our text-to-speech engine into an embedded system. From experimental results, it developed that the run-time work load for the test sentences was 80 MIPS approximately and the implemented engine was useful and scalable for mid-class embedded system.