For the last couple of years, the field of speech synthesis has improved dramatically thanks to deep learning. There are more and more deep learning-based TTS systems developed to make it possible to produce voices with high intelligibility and naturalness. Meanwhile, controlling the expressiveness is yet a big deal, generating speech in different styles or manners has received a lot of attention from community recently. This paper aims to give our solutions to deal with the task emotional speech synthesis (ESS) at VLSP 2022 which allows to generate humanlike natural-sounding voice from a given input text with desired emotional expression. By integrating speaker embedding, prosody bottleneck into FastSpeech 2, our systems can promisingly generate emotional speech of a single speaker (Sub-task 1), transfer speaking styles from another speaker to the target speaker with neutral non-expressive data while retaining the target speaker's identity (Sub-task 2).

翻译：近年来，得益于深度学习技术，语音合成领域取得了显著进展。基于深度学习的文本转语音（TTS）系统日益增多，能够生成具有高可懂度和自然度的语音。然而，控制语音表现力仍是一大挑战，如何以不同风格或方式生成语音近来受到了学界的广泛关注。本文旨在提出我们在VLSP 2022情感语音合成（ESS）任务中的解决方案，该任务要求根据输入文本生成具有指定情感表达的自然逼真语音。通过将说话人嵌入、韵律瓶颈特征集成至FastSpeech 2中，我们的系统有望实现：子任务1）生成单一说话人的情感语音；子任务2）在保留目标说话人身份的前提下，将另一个说话人的说话风格迁移至仅含中性无表现力语音数据的目标说话人。