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

2021-04-03 13:52:47
Rui Liu, Berrak Sisman, Haizhou Li
arXiv_CL
               
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.

Abstract (translated)

URL

https://arxiv.org/abs/2104.01408

PDF

https://arxiv.org/pdf/2104.01408.pdf

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