Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability

• URL: http://arxiv.org/abs/2104.01408v1
• Date: Sat, 3 Apr 2021 13:52:47 GMT
• Title: Reinforcement Learning for Emotional Text-to-Speech Synthesis with Improved Emotion Discriminability
• Authors: Rui Liu, Berrak Sisman, Haizhou Li
• Abstract summary: Emotional text-to-speech synthesis (ETTS) has seen much progress in recent years. We propose a new interactive training paradigm for ETTS, denoted as i-ETTS. We formulate an iterative training strategy with reinforcement learning to ensure the quality of i-ETTS optimization.
• Score: 82.39099867188547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Emotional text-to-speech synthesis (ETTS) has seen much progress in recent years. However, the generated voice is often not perceptually identifiable by its intended emotion category. To address this problem, we propose a new interactive training paradigm for ETTS, denoted as i-ETTS, which seeks to directly improve the emotion discriminability by interacting with a speech emotion recognition (SER) model. Moreover, we formulate an iterative training strategy with reinforcement learning to ensure the quality of i-ETTS optimization. Experimental results demonstrate that the proposed i-ETTS outperforms the state-of-the-art baselines by rendering speech with more accurate emotion style. To our best knowledge, this is the first study of reinforcement learning in emotional text-to-speech synthesis.

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