The Codec Language Model-Based Zero-Shot Spontaneous Style TTS System for CoVoC Challenge 2024
Year: 2024
Type: conference-paper
Abstract: This paper describes the zero-shot spontaneous style TTS system for the ISCSLP 2024 Conversational Voice Clone Chal-lenge (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 gen-erate 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.
Authors: Shuoyi Zhou, Yixuan Zhou, Weiqing Li., Jun Chen, Runchuan Ye, Weihao Wu, Zijian Lin, Shun Lei, Zhiyong Wu
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G2603945996
OpenAlex ID: W4405709167