This paper raises the issue of speech database reduction adapted to a specific domain for Text-To-Speech (TTS) synthesis application. We evaluate several methods: a database pruning technique based on the statistical behaviour of the unit selection algorithm and a novel method based on the Kullback- Leibler divergence. The aim of the former method is to eliminate the least selected units during the synthesis of a domain specific training corpus. The aim of the latter approach is to build a reduced database whose unit distribution approximates a given target distribution. We compare the reduced databases. Finally we evaluate these methods on several objective measures given by the unit selection algorithm.