@conference {2024,
	title = {Accent Conversion in Text-To-Speech Using Multi-Level VAE and Adversarial Training},
	booktitle = {Proc. of IEEE Tencon, Singapore},
	year = {2024},
	abstract = {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.},
	url = {https://arxiv.org/abs/2406.01018},
	author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans}
}
@conference {2022,
	title = {Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder},
	booktitle = {Proc. of IEEE Tencon, Singapore},
	year = {2024},
	abstract = {Accent plays a significant role in speech communication, influencing understanding capabilities and also conveying a person{\textquoteright}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{\textquoteright}s speech that is converted to any desired target accent. Our thorough experiments validate the effectiveness of our 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.},
	url = {https://arxiv.org/abs/2211.03316},
	author = {J. Melechovsky and A. Mehrish and B. Sisman and D. Herremans}
}
