Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder

2022/11/07 by Jan Melechovský, Ambuj Mehrish, Melechovsky, Jan +5 · 1 citation
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Natural Language Processing Techniques #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and dialogue systems #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2211.03316

openalex publication_date 2022/11/07 · openalex created_date 2022/11/13 · openalex updated_date 2026/07/28

Abstract

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, and convert this 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 model's ability to manipulate accents in the synthesized speech. Overall, our proposed framework presents a promising avenue for future accented TTS research.

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• Scalable Controllable Accented TTS

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