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In the context of the French speech database creation project, 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 database are also discussed.

In this study we investigate whether a classification algorithm originally designed for authorship verification can be used to classify speakers according to their gender, age, regional background and level of education by investigating the lexical content and the pronunciation of their speech. Contrary to other speaker classification techniques, our algorithm does not base its decisions on direct measurements of the speech signal; rather it learns characteristic speech features of speaker classes by analysing the orthographic and broad phonetic transcription of speech from members of these classes. The resulting class profiles are subsequently used to verify whether unknown speakers belong to these classes.