Online speaker de-identification using voice transformation

Speaker de-identification is the process by which speech is transformed in a way that the speaker identity is masked, while at the same time the transformed speech preserves acoustic information that contributes to the intelligibility, naturalness and clarity. Systems that perform speech de-identification could be used in voice driven applications (for example in call centres) where the speaker's identity has to be hidden. The paper describes the experiments we have performed in order to de-identify speech using GMM based voice transformation techniques and speaker identification using freely available tools. We propose a method by which speakers whose speech has not been used to build voice transformations (for training) can be efficiently de-identified online. The proposed method is evaluated using a speech database of read speech and a small set of speakers. The results we present show that the proposed de-identification method performs similarly as a closed-set de-identification procedure that requires previous enrolment and can efficiently be used for online speaker de-identification.