BERTIVITS: The Posterior Encoder Fusion of Pre-Trained Models and Residual Skip Connections for End-to-End Speech Synthesis

Enhancing the naturalness and rhythmicity of generated audio in end-to-end speech synthesis is crucial. The current state-of-the-art (SOTA) model, VITS, utilizes a conditional variational autoencoder architecture. However, it faces challenges, such as limited robustness, due to training solely on te...

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Autors principals:	Zirui Wang, Minqi Song, Dongbo Zhou
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2024-06-01
Col·lecció:	Applied Sciences
Matèries:	pre-trained model text to speech neural TTS speech synthesis end-to-end model
Accés en línia:	https://www.mdpi.com/2076-3417/14/12/5060
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