Re-ENACT：基于 Actor-Critic 策略的强化学习情感语音生成
Re-ENACT: Reinforcement Learning for Emotional Speech Generation using Actor-Critic Strategy
AI总结：
提出 Re-ENACT，用贝叶斯片段选择与 actor-critic 强化学习修改语音韵律，实现目标情感转换，并达到需成对训练的先进模型水平。
摘要：
本文提出首个利用 actor-critic 强化学习策略修改给定语音信号韵律特征的方法。该方法采用贝叶斯框架识别连续的重要片段，并将给定话语的片段与人类情感感知相联系。作者训练神经网络生成一组 Bernoulli 随机变量的变分后验；模型对其施加 Markov 先验以保证连续性。从该分布中采样的结果用于下游情感预测。此外，神经网络还被训练以预测情感类别上的软分配作为目标变量。下一步，作者修改被掩蔽片段的韵律特征（音高、强度和节奏），以提高目标情感得分。研究采用 actor-critic 强化学习，通过离散化修改空间来训练韵律修改器；该方法还为通过 WSOLA 操作进行节奏操控时的梯度计算问题提供了简单解决方案。实验表明，该框架能够将给定语音话语的感知情感改变为目标情感；同时证明，这种统一技术可与需要成对训练的监督和无监督领域最先进情感转换模型相媲美。
英文摘要：
In this paper, we propose the first method to modify the prosodic features of a given speech signal using actor-critic reinforcement learning strategy. Our approach uses a Bayesian framework to identify contiguous segments of importance that links segments of the given utterances to perception of emotions in humans. We train a neural network to produce the variational posterior of a collection of Bernoulli random variables; our model applies a Markov prior on it to ensure continuity. A sample from this distribution is used for downstream emotion prediction. Further, we train the neural network to predict a soft assignment over emotion categories as the target variable. In the next step, we modify the prosodic features (pitch, intensity, and rhythm) of the masked segment to increase the score of target emotion. We employ an actor-critic reinforcement learning to train the prosody modifier by discretizing the space of modifications. Further, it provides a simple solution to the problem of gradient computation through WSOLA operation for rhythm manipulation. Our experiments demonstrate that this framework changes the perceived emotion of a given speech utterance to the target. Further, we show that our unified technique is on par with state-of-the-art emotion conversion models from supervised and unsupervised domains that require pairwise training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
