Optimizing the coverage of a speech database through a selection of representative speaker recordings
In the context of the Neologos French speech database creation project,1 a general methodology was defined for the selection of representative speaker recordings. The selection aims at providing a good coverage in terms of speaker variability while limiting the number of recorded speakers. This is intended to make the resulting database both more adapted to the development of recently proposed multi-model methods and less expensive to collect. The presented methodology proposes a selection process based on the optimization of a quality criterion defined in a variety of speaker similarity modeling frameworks. The selection can be achieved with respect to a unique similarity criterion, using classical clustering methods such as hierarchical or K-medians clustering, or it can combine several speaker similarity criteria, thanks to a newly developed clustering method called focal speakers selection. In this framework, four different speaker similarity criteria are tested, and three different speaker clustering algorithms are compared. Results pertaining to the collection of the Neologos database are also discussed.
speech database multi-models speaker selection speaker clustering cost minimization optimal coverage speech and speaker recognition speech synthesis