Poster
Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion
Joan Serrà · Santiago Pascual · Carlos Segura Perales

Wed Dec 11th 05:00 -- 07:00 PM @ East Exhibition Hall B + C #74

End-to-end models for raw audio generation are a challenge, specially if they have to work with non-parallel data, which is a desirable setup in many situations. Voice conversion, in which a model has to impersonate a speaker in a recording, is one of those situations. In this paper, we propose Blow, a single-scale normalizing flow using hypernetwork conditioning to perform many-to-many voice conversion between raw audio. Blow is trained end-to-end, with non-parallel data, on a frame-by-frame basis using a single speaker identifier. We show that Blow compares favorably to existing flow-based architectures and other competitive baselines, obtaining equal or better performance in both objective and subjective evaluations. We further assess the impact of its main components with an ablation study, and quantify a number of properties such as the necessary amount of training data or the preference for source or target speakers.

Author Information

Joan Serrà (Dolby Laboratories)
Santiago Pascual (Universitat Politècnica de Catalunya)
Carlos Segura Perales (Telefónica Research)