New experiments on speaker diarization for unsupervised speaking style voice building for speech synthesis

Universal use of speech synthesis in different applications would require an easy development of new voices with little manual intervention. Considering the amount of multimedia data available on internet and media, one interesting goal is to develop tools and methods to automatically build multi-style voices from them. In a previous paper a methodology for constructing such tools was sketched, and preliminary experiments with a multi-style database were presented. In this paper we further investigate such approach and propose several improvements to it based on the selection of the appropriate number of initial speakers, the use or not of noise reduction filters, the use of the F0 feature and the use of a music detection algorithm. We have demonstrated that the best system using music detection algorithm decreases the precision error 22.36% relative for the development set and 39.64% relative for the test set compared to the baseline, without degrading the merit factor. The average precision for the test set is 90.62% ranging from 76.18% for reportages to 99.93% for meteorology reports.