Transferring Source Style in Non-Parallel Voice Conversion

Abstract

Voice conversion (VC) techniques aim to modify speaker identity of an utterance while preserving the underlying linguistic information. Most VC approaches ignore modeling of the speaking style (e.g. emotion and emphasis), which may contain the factors intentionally added by the speaker and should be retained during conversion. This study proposes a sequence-to-sequence based non-parallel VC approach, which has the capability of transferring the speaking style from the source speech to the converted speech by explicitly modeling. Objective evaluation and subjective listening tests show superiority of the proposed VC approach in terms of speech naturalness and speaker similarity of the converted speech. Experiments are also conducted to show the source-style transferability of the proposed approach.

arXiv comment5 pages, 8 figures, submitted to INTERSPEECH 2020

References in corpus (7)

• Sequence Level Training with Recurrent Neural Networks
• A Comparative Study on Transformer vs RNN in Speech Applications
• FastSpeech: Fast, Robust and Controllable Text to Speech
• Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
• Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks
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Cited by in corpus (2)

• Any-to-Many Voice Conversion with Location-Relative Sequence-to-Sequence Modeling
• An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning