  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.
