C. Müller (Germany)
speaker classification, age and gender recognition, dynamic Bayesian networks, machine learning
This paper describes the two-layered AGENDER speaker classification approach which primarily recognizes the speaker’s age and gender but also incorporates novel domain-independent pattern classification aspects which can be applied to acquire other speaker characteristics like emotions or cognitive load. The described approach dis tinguishes itself by means of a special post-processing technique: On the so called Second Layer, multiple post processing problems are solved with one single mecha nism, namely dynamic Bayesian networks (DBNs). While the actual classification aspect – the First Layer – is merely summarized, this paper focuses on the description of the Second Layer. Particularly, examples are provided on how DBNs can be used for: explicitely modeling the classifica tion inherent uncertainty, incorporating top down knowl edge into the decision making process, and fusing the re sults of multiple classifiers. The paper finishes with a sum mary as well as an outlook to future extensions.
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