Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations

• URL: http://arxiv.org/abs/2010.12231v1
• Date: Fri, 23 Oct 2020 08:34:52 GMT
• Title: Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations
• Authors: Wen-Chin Huang, Yi-Chiao Wu, Tomoki Hayashi, Tomoki Toda
• Abstract summary: We present a novel approach to any-to-one (A2O) voice conversion (VC) in a sequence-to-sequence framework. A2O VC aims to convert any speaker, including those unseen during training, to a fixed target speaker.
• Score: 49.55361944105796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel approach to any-to-one (A2O) voice conversion (VC) in a sequence-to-sequence (seq2seq) framework. A2O VC aims to convert any speaker, including those unseen during training, to a fixed target speaker. We utilize vq-wav2vec (VQW2V), a discretized self-supervised speech representation that was learned from massive unlabeled data, which is assumed to be speaker-independent and well corresponds to underlying linguistic contents. Given a training dataset of the target speaker, we extract VQW2V and acoustic features to estimate a seq2seq mapping function from the former to the latter. With the help of a pretraining method and a newly designed postprocessing technique, our model can be generalized to only 5 min of data, even outperforming the same model trained with parallel data.

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