Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion

• URL: http://arxiv.org/abs/2312.17508v1
• Date: Fri, 29 Dec 2023 08:06:45 GMT
• Title: Attention-based Interactive Disentangling Network for Instance-level Emotional Voice Conversion
• Authors: Yun Chen, Lingxiao Yang, Qi Chen, Jian-Huang Lai, Xiaohua Xie
• Abstract summary: Emotional Voice Conversion aims to manipulate a speech according to a given emotion while preserving non-emotion components. We propose an Attention-based Interactive diseNtangling Network (AINN) that leverages instance-wise emotional knowledge for voice conversion.
• Score: 81.1492897350032
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotional Voice Conversion aims to manipulate a speech according to a given emotion while preserving non-emotion components. Existing approaches cannot well express fine-grained emotional attributes. In this paper, we propose an Attention-based Interactive diseNtangling Network (AINN) that leverages instance-wise emotional knowledge for voice conversion. We introduce a two-stage pipeline to effectively train our network: Stage I utilizes inter-speech contrastive learning to model fine-grained emotion and intra-speech disentanglement learning to better separate emotion and content. In Stage II, we propose to regularize the conversion with a multi-view consistency mechanism. This technique helps us transfer fine-grained emotion and maintain speech content. Extensive experiments show that our AINN outperforms state-of-the-arts in both objective and subjective metrics.

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