A discriminative approach for speaker selection in speaker de-identification systems

Speaker de-identification is an interesting and newly investigated task in speech processing. In the current implementations, this task is based on transforming one speaker speech to another speaker in order to hide the speaker identity. In this paper we present a discriminative approach for human speaker selection for speaker de-identification. We used two modules, a speaker identification system and a speaker transformation one, to select the most appropriate speaker to transform the source speaker speech from a set of speakers. In order to select the target speaker, we minimize the identification confidence of the transformed speech as the source speaker and maximize the confusion about the transformed speech membership to the rest of the speaker models and the identiication conidence of the re-transformed speech using the source speaker model. These three factors are combined to achieve overall optimization performance in order to select the best target speaker to transform the source.