Experiments of automatic speaker recognition in open sets

Abstract

In automatic speaker recognition tasks a situation may occur in which it cannot be assumed that a voice to be recognized belongs to a known set of voice classes (a closed set). Thus, a problem arises with respect to working out a recognition algorithm that could operate in open sets of speakers, i.e. without assuming that a speech sample from an unknown speaker must belong to one of the speakers of a given set. This paper presents together with some relevant experiments a method for automatic speaker recognition in open sets. This method ensures the effective rejection of outsiders. Two “key words” were examined, as were different parameter sets and large and small populations of speakers. The methodological assumptions and experimental results show that the proposed open set voice recognition method is very flexible and makes it possible to adjust global characteristics, i.e. α and β errors, to the strategy adopted by the recognition system. For a given set of voice patterns, it is always possible to optimize recognition by a proper selection of approximations of the ground class distribution, i.e. by an appropriate selection of decision thresholds.