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Regional accents of the same language affect not only how words arepronounced (i.e., phonetic content), but also impact prosodic aspects of speechsuch as speaking rate and intonation. This paper investigates a novelflow-based approach to accent conversion using normalizing flows. The proposedapproach revolves around three steps: remapping the phonetic conditioning, tobetter match the target accent, warping the duration of the converted speech,to better suit the target phonemes, and an attention mechanism that implicitlyaligns source and target speech sequences. The proposed remap-warp-attendsystem enables adaptation of both phonetic and prosodic aspects of speech whileallowing for source and converted speech signals to be of different lengths.Objective and subjective evaluations show that the proposed approachsignificantly outperforms a competitive CopyCat baseline model in terms ofsimilarity to the target accent, naturalness and intelligibility.

Remap, warp and attend: Non-parallel many-to-many accent conversion with Normalizing Flows