Rhythm-Flexible Voice Conversion without Parallel Data Using Cycle-GAN over Phoneme Posteriorgram Sequences

TL;DR

This paper introduces a novel rhythm-flexible voice conversion method that leverages an unsupervised Cycle-GAN with sequence-to-sequence modeling to transform phoneme posteriorgram sequences without parallel data, removing length constraints.

Contribution

It proposes a new approach combining Cycle-GAN and sequence-to-sequence models for non-parallel, rhythm-flexible voice conversion that overcomes previous length and data limitations.

Findings

Encouraging results on two datasets.

Effective removal of length constraints.

Successful transformation of phoneme sequences without parallel data.

Abstract

Speaking rate refers to the average number of phonemes within some unit time, while the rhythmic patterns refer to duration distributions for realizations of different phonemes within different phonetic structures. Both are key components of prosody in speech, which is different for different speakers. Models like cycle-consistent adversarial network (Cycle-GAN) and variational auto-encoder (VAE) have been successfully applied to voice conversion tasks without parallel data. However, due to the neural network architectures and feature vectors chosen for these approaches, the length of the predicted utterance has to be fixed to that of the input utterance, which limits the flexibility in mimicking the speaking rates and rhythmic patterns for the target speaker. On the other hand, sequence-to-sequence learning model was used to remove the above length constraint, but parallel training data are needed. In this paper, we propose an approach utilizing sequence-to-sequence model trained with unsupervised Cycle-GAN to perform the transformation between the phoneme posteriorgram sequences for different speakers. In this way, the length constraint mentioned above is removed to offer rhythm-flexible voice conversion without requiring parallel data. Preliminary evaluation on two datasets showed very encouraging results.