Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversion
summary: Voice conversion as the style transfer task applied to speech, refers to
converting one person's speech into a new speech that sounds like another
person's. Up to now, there has been a lot of research devoted to better
implementation of VC tasks. However, a good voice conversion model should not
only match the timbre information of the target speaker, but also expressive
information such as prosody, pace, pause, etc. In this context, prosody
modeling is crucial for achieving expressive voice conversion that sounds
natural and convincing. Unfortunately, prosody modeling is important but
challenging, especially without text transcriptions. In this paper, we firstly
propose a novel voice conversion framework named 'PMVC', which effectively
separates and models the content, timbre, and prosodic information from the
speech without text transcriptions. Specially, we introduce a new speech
augmentation algorithm for robust prosody extraction. And building upon this,
mask and predict mechanism is applied in the disentanglement of prosody and
content information. The experimental results on the AIShell-3 corpus supports
our improvement of naturalness and similarity of converted speech.
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Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversion
summary: Voice conversion as the style transfer task applied to speech, refers to converting one person's speech into a new speech that sounds like another person's. Up to now, there has been a lot of research devoted to better implementation of VC tasks. However, a good voice conversion model should not only match the timbre information of the target speaker, but also expressive information such as prosody, pace, pause, etc. In this context, prosody modeling is crucial for achieving expressive voice conversion that sounds natural and convincing. Unfortunately, prosody modeling is important but challenging, especially without text transcriptions. In this paper, we firstly propose a novel voice conversion framework named 'PMVC', which effectively separates and models the content, timbre, and prosodic information from the speech without text transcriptions. Specially, we introduce a new speech augmentation algorithm for robust prosody extraction. And building upon this, mask and predict mechanism is applied in the disentanglement of prosody and content information. The experimental results on the AIShell-3 corpus supports our improvement of naturalness and similarity of converted speech.
id: http://arxiv.org/abs/2308.11084v1
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