CrossAccent-TTS：通过解耦说话人和口音表示实现跨语言口音强度可控文本转语音
CrossAccent-TTS: Cross-Lingual Accent-Intensity Controllable Text-to-Speech via Disentangled Speaker and Accent Representations
AI总结：
提出CrossAccent-TTS框架，通过口音强度控制器（AIC）注入加权语言嵌入到口音子空间，实现跨语言口音控制与转换，同时保持说话人身份，在Indic Multilingual和L2-arctic数据集上优于基线。
摘要：
口音转换和可控性仍然是跨语言文本转语音（TTS）中的基本挑战，特别是在低资源和语音多样性高的印度语言中。虽然最近基于大语言模型（LLM）的TTS系统表现出强大的跨语言泛化能力，但它们对口音特征和强度的显式控制有限。在本文中，我们提出CrossAccentTTS，一个既能实现口音控制又能实现口音转换同时保持说话人身份的框架。具体来说，我们引入了一个口音强度控制器（AIC），它将加权语言嵌入注入到口音子空间中，允许在推理时实现口音之间的平滑插值和口音强度的细粒度调节。在Indic Multilingual和L2-arctic数据集上的实验表明，CrossAccent-TTS实现了对口音强度的精确控制，通过保持说话人相似性和自然度，在口音相似性和可控性方面优于强基线。
英文摘要：
Accent conversion and controllability remain fundamental challenges in cross-lingual text-to-speech (TTS), particularly for low-resource and phonetically diverse Indic languages. While recent large language model (LLM)-based TTS systems exhibit strong cross-lingual generalization, they provide limited explicit control over accent characteristics and intensity. In this paper, we propose CrossAccentTTS, a framework that enables both accent control and conversion while preserving speaker identity. Specifically, we introduce an Accent Intensity Controller (AIC) that injects weighted language embeddings into the accent subspace, allowing smooth interpolation between accents and fine-grained modulation of accent strength at inference time. Experiments on the Indic Multilingual and L2-arctic datasets shows that CrossAccent-TTS achieves precise control of accent intensity, outperforming strong baselines in accent similarity and controllability by maintaining speaker similarity and naturalness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
