Accent Conversion Using Phonetic Posteriorgrams

Abstract

Accent conversion (AC) aims to transform non-native speech to sound as if the speaker had a native accent. This can be achieved by mapping source spectra from a native speaker into the acoustic space of the non-native speaker. In prior work, we proposed an AC approach that matches frames between the two speakers based on their acoustic similarity after compensating for differences in vocal tract length. In this paper, we propose an approach that matches frames between the two speakers based on their phonetic (rather than acoustic) similarity. Namely, we map frames from the two speakers into a phonetic posteriorgram using speaker-independent acoustic models trained on native speech. We evaluate the proposed algorithm on a corpus containing multiple native and non-native speakers. Compared to the previous AC algorithm, the proposed algorithm improves the ratings of acoustic quality (20% increase in mean opinion score) and native accent (69% preference) while retaining the voice identity of the non-native speaker.