Accent plays a significant role in speech communication, influencing one's capability to understand as well as conveying a person's identity. This paper introduces a novel and efficient framework for accented Text-to-Speech (TTS) synthesis based on a Conditional Variational Autoencoder. It has the ability to synthesize a selected speaker's voice, which is converted to any desired target accent. Our thorough experiments validate the effectiveness of the proposed framework using both objective and subjective evaluations. The results also show remarkable performance in terms of the ability to manipulate accents in the synthesized speech and provide a promising avenue for future accented TTS research.

翻译：口音在语音交流中扮演着重要角色，既影响听者的理解能力，也传递着说话者的身份信息。本文提出了一种基于条件变分自编码器的新型高效带口音文本转语音合成框架。该框架能够合成选定说话人的语音，并将其转换为任意目标口音。我们通过主客观评估相结合的全面实验验证了所提框架的有效性。实验结果同时表明，该框架在合成语音的口音操控方面表现优异，为未来带口音文本转语音研究提供了有前景的技术路径。