发表机构
Seoul National University; University of Seoul（首尔大学; 首尔市立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对代码切换文本到语音的重音偏差问题，提出无训练的短语定位语言对比引导框架，通过自注意力探测定位短语边界，实现各语言区域重音的准确控制，提升代码切换语音的原生度与自然度。
AI 中文摘要
当前语音合成在代码切换场景中存在困难，代码切换指将外语短语混入母语语句，导致该短语采用母语重音而非其原生重音。本文提出短语定位的语言对比引导（Language-Contrastive Guidance, LCG），这是一种无训练的推理框架，用于在跨语言文本到语音中恢复代码切换短语的原生重音。LCG将应用于整个语句的单一语言引导替换为针对每个区域的单独引导，使各部分受自身语言引导。为选择应用该本地化引导的位置，本文提出一种自注意力探测技术，无需外部对齐即可找到短语边界。这些组件共同生成的语音中，每个区域带有自身语言的重音，无需微调或辅助模型。在不同语言对中，LCG可显著提升代码切换短语的原生度，同时抑制重音泄漏，并保留整体说话人身份与自然度。
英文摘要
Current speech synthesis struggles with code-switching, which mixes a foreign language phrase into a primary language utterance, causing the phrase to be spoken with the primary language's accent rather than its native one. We propose Phrase-Localized Language-Contrastive Guidance (LCG), a training-free inference framework that restores a native accent to code-switched phrases in cross-lingual text-to-speech. LCG replaces the single language guidance applied across the whole utterance with a separate guidance for each region, so each part is guided by its own language. To choose where to apply this localized guidance, we propose a self-attention probing technique that finds the phrase boundaries without external alignments. Together, these components generate speech in which each region carries the accent of its own language, requiring no fine-tuning or auxiliary models. Across diverse language pairs, LCG robustly increases the nativeness of the code-switched phrase while suppressing accent leakage, and preserving overall speaker identity and naturalness.
CommentsAccepted to EMNLP 2026 (Main Conference). Demo: https://saga1214.github.io/PhraseLocalizedLCG/
