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Speaking rate refers to the average number of phonemes within some unit time,while the rhythmic patterns refer to duration distributions for realizations ofdifferent phonemes within different phonetic structures. Both are keycomponents of prosody in speech, which is different for different speakers.Models like cycle-consistent adversarial network (Cycle-GAN) and variationalauto-encoder (VAE) have been successfully applied to voice conversion taskswithout parallel data. However, due to the neural network architectures andfeature vectors chosen for these approaches, the length of the predictedutterance has to be fixed to that of the input utterance, which limits theflexibility in mimicking the speaking rates and rhythmic patterns for thetarget speaker. On the other hand, sequence-to-sequence learning model was usedto remove the above length constraint, but parallel training data are needed.In this paper, we propose an approach utilizing sequence-to-sequence modeltrained with unsupervised Cycle-GAN to perform the transformation between thephoneme posteriorgram sequences for different speakers. In this way, the lengthconstraint mentioned above is removed to offer rhythm-flexible voice conversionwithout requiring parallel data. Preliminary evaluation on two datasets showedvery encouraging results.

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