标题： 并行GPT：为零样本文本到语音协调声学和语义信息的独立性和相互依赖性 显示英文标题

标题： Parallel GPT: Harmonizing the Independence and Interdependence of Acoustic and Semantic Information for Zero-Shot Text-to-Speech

摘要： 语音表示和大型语言模型的进步提高了零样本文本到语音（TTS）的性能。 然而，现有的零样本TTS模型在捕捉声学和语义特征之间的复杂相关性方面面临挑战，导致表现力和相似性不足。 主要原因是语义和声学特征之间的复杂关系，表现为独立性和相互依赖性两个方面。本文介绍了一个结合自回归（AR）和非自回归（NAR）模块的TTS框架，以协调声学和语义信息的独立性和相互依赖性。 AR模型利用提出的并行分词器同时合成顶级语义和声学标记。 相比之下，考虑到相互依赖性，耦合的NAR模型基于通用AR模型的输出预测详细标记。 基于此架构构建的并行GPT，旨在通过其并行结构提高零样本文本到语音合成的效果。 在英语和中文数据集上的实验表明，所提出的模型在合成质量和效率方面显著优于现有零样本TTS模型。 语音演示可在 https://t1235-ch.github.io/pgpt/ 上查看。 显示英文摘要

摘要： Advances in speech representation and large language models have enhanced zero-shot text-to-speech (TTS) performance. However, existing zero-shot TTS models face challenges in capturing the complex correlations between acoustic and semantic features, resulting in a lack of expressiveness and similarity. The primary reason lies in the complex relationship between semantic and acoustic features, which manifests independent and interdependent aspects.This paper introduces a TTS framework that combines both autoregressive (AR) and non-autoregressive (NAR) modules to harmonize the independence and interdependence of acoustic and semantic information. The AR model leverages the proposed Parallel Tokenizer to synthesize the top semantic and acoustic tokens simultaneously. In contrast, considering the interdependence, the Coupled NAR model predicts detailed tokens based on the general AR model's output. Parallel GPT, built on this architecture, is designed to improve zero-shot text-to-speech synthesis through its parallel structure. Experiments on English and Chinese datasets demonstrate that the proposed model significantly outperforms the quality and efficiency of the synthesis of existing zero-shot TTS models. Speech demos are available at https://t1235-ch.github.io/pgpt/.