Adaptive database reduction for domain specific speech synthesis
نویسندگان
چکیده
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 KullbackLeibler 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.
منابع مشابه
Approaches for adaptive database reduction for text-to-speech synthesis
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 ...
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