With rapid globalization, the need to build inclusive and representative
speech technology cannot be overstated. Accent is an important aspect of speech
that needs to be taken into consideration while building inclusive speech
synthesizers. Inclusive speech technology aims to erase any biases towards
specific groups, such as people of certain accent. We note that
state-of-the-art Text-to-Speech (TTS) systems may currently not be suitable for
all people, regardless of their background, as they are designed to generate
high-quality voices without focusing on accent. In this paper, we propose a TTS
model that utilizes a Multi-Level Variational Autoencoder with adversarial
learning to address accented speech synthesis and conversion in TTS, with a
vision for more inclusive systems in the future. We evaluate the performance
through both objective metrics and subjective listening tests. The results show
an improvement in accent conversion ability compared to the baseline.

提出了一种利用多级变分自编码器与对抗学习的文本转语音模型，用于解决语音合成和转换中的口音问题，以期构建更具包容性的系统。通过客观评价指标和主观听觉测试对性能进行评估，结果表明与基线相比，口音转换能力有所提高。