HierVST: Hierarchical Adaptive Zero-shot Voice Style Transfer

• URL: http://arxiv.org/abs/2307.16171v1
• Date: Sun, 30 Jul 2023 08:49:55 GMT
• Title: HierVST: Hierarchical Adaptive Zero-shot Voice Style Transfer
• Authors: Sang-Hoon Lee, Ha-Yeong Choi, Hyung-Seok Oh, Seong-Whan Lee
• Abstract summary: We present HierVST, a hierarchical adaptive end-to-end zero-shot VST model. Without any text transcripts, we only use the speech dataset to train the model. With a hierarchical adaptive structure, the model can adapt to a novel voice style and convert speech progressively.
• Score: 25.966328901566815
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Despite rapid progress in the voice style transfer (VST) field, recent zero-shot VST systems still lack the ability to transfer the voice style of a novel speaker. In this paper, we present HierVST, a hierarchical adaptive end-to-end zero-shot VST model. Without any text transcripts, we only use the speech dataset to train the model by utilizing hierarchical variational inference and self-supervised representation. In addition, we adopt a hierarchical adaptive generator that generates the pitch representation and waveform audio sequentially. Moreover, we utilize unconditional generation to improve the speaker-relative acoustic capacity in the acoustic representation. With a hierarchical adaptive structure, the model can adapt to a novel voice style and convert speech progressively. The experimental results demonstrate that our method outperforms other VST models in zero-shot VST scenarios. Audio samples are available at \url{https://hiervst.github.io/}.

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