The Codec Language Model-based Zero-Shot Spontaneous Style TTS System for CoVoC Challenge 2024

Autor:	Zhou, Shuoyi, Zhou, Yixuan, Li, Weiqing, Chen, Jun, Ye, Runchuan, Wu, Weihao, Lin, Zijian, Lei, Shun, Wu, Zhiyong
Rok vydání:	2024
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	This paper describes the zero-shot spontaneous style TTS system for the ISCSLP 2024 Conversational Voice Clone Challenge (CoVoC). We propose a LLaMA-based codec language model with a delay pattern to achieve spontaneous style voice cloning. To improve speech intelligibility, we introduce the Classifier-Free Guidance (CFG) strategy in the language model to strengthen conditional guidance on token prediction. To generate high-quality utterances, we adopt effective data preprocessing operations and fine-tune our model with selected high-quality spontaneous speech data. The official evaluations in the CoVoC constrained track show that our system achieves the best speech naturalness MOS of 3.80 and obtains considerable speech quality and speaker similarity results. Comment: Accepted by ISCSLP 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.01100 Zobrazit plný text záznamu View this record from Arxiv