Domain adaptation for TTS systems

This paper puts forward a domain adaptation problem that has not been studied well. For corpus-driven TTS systems, domain adaptation is realized by adding a small amount of domain-specific speech that will provide the maximum increase in average length of units that are used for synthesizing speech in that domain. An approach for generating optimized script for adaptation is proposed, the core of which is a dynamic programming based algorithm that segments domain-specific corpus into minimum number of segments that appear in the unit inventory. Increase in MOS after adaptation can be estimated from the generated script without recording speech from it. The results show that the amount of MOS increase depends not only on the size of the training set and the size of the script for adaptation, but also on the broadness of the domain. Narrower domains have larger increase in MOS.