Total: 1
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 database adaptation method based on the Kullback-Leibler divergence. The aim of the former is to eliminate the least selected units during the synthesis of a domain specific training corpus. The aim of the later approach is to build a reduced database whose unit distribution approximates a given target distribution. We evaluate these methods on several objective measures.