Voice convergin: Speaker de-identification by voice transformation

Speaker identification might be a suitable answer to prevent unauthorized access to personal data. However we also need to provide solutions to secure transmission of spoken information. This challenge divides into two major aspects. First, the secure transmission of the content of the spoken input and second the secure transmission of the identity of the speaker. In this paper we concentrate on the latter, i.e. how to securely transmit information via voice without revealing the identity of the speaker to unauthorized listeners. In order to make the first steps toward solving this problem we study in this paper the potential of voice transformation for speaker de-identification. We use two speaker identification approaches to verify the success of de-identification with voice transformation, a GMM-based and a Phonetic approach, and study different voice transformation strategies to disguise speaker identity information while preserving understandability.