SFMS-ALR: Script-First Multilingual Speech Synthesis with Adaptive Locale Resolution（脚本优先的自适应区域设置解析多语种语音合成）
SFMS-ALR: Script-First Multilingual Speech Synthesis with Adaptive Locale Resolution
AI总结：
针对句内多语种语音合成的语言突变、韵律不匹配等问题，提出无需重训、引擎无关的SFMS-ALR框架，通过脚本分段、自适应语言识别和情感韵律调整实现流畅实时的语码转换语音生成，兼具灵活性与可部署性。
摘要：
句内多语种语音合成（语码转换TTS）因语言突变、书写系统多样以及语言间韵律不匹配，仍是一项重大挑战。传统TTS系统通常为单语种，无法在混合语言语境下生成自然、可懂的语音。我们提出脚本优先的自适应区域设置解析多语种合成（SFMS-ALR），这是一个引擎无关的框架，用于生成流畅、实时的语码转换语音。SFMS-ALR按Unicode脚本对输入文本分段，采用自适应语言识别确定每个分段的语言和区域设置，并通过情感感知调整归一化韵律，以保持跨语言的表达连续性。该算法生成带有适当“lang”或“voice”跨度的统一SSML表示，并在单个TTS请求中合成话语。与端到端多语种模型不同，SFMS-ALR无需重新训练，可与Google、Apple、Amazon及其他提供商的现有语音无缝集成。与Unicom和Mask LID等数据驱动流水线的对比分析表明，SFMS-ALR具有灵活性、可解释性和即时可部署性。该框架为高质量、引擎无关的多语种TTS建立了模块化基线，并提出了可懂度、自然度和用户偏好的评估策略。
英文摘要：
Intra-sentence multilingual speech synthesis (code-switching TTS) remains a major challenge due to abrupt language shifts, varied scripts, and mismatched prosody between languages. Conventional TTS systems are typically monolingual and fail to produce natural, intelligible speech in mixed-language contexts. We introduce Script-First Multilingual Synthesis with Adaptive Locale Resolution (SFMS-ALR), an engine-agnostic framework for fluent, real-time code-switched speech generation. SFMS-ALR segments input text by Unicode script, applies adaptive language identification to determine each segment's language and locale, and normalizes prosody using sentiment-aware adjustments to preserve expressive continuity across languages. The algorithm generates a unified SSML representation with appropriate "lang" or "voice" spans and synthesizes the utterance in a single TTS request. Unlike end-to-end multilingual models, SFMS-ALR requires no retraining and integrates seamlessly with existing voices from Google, Apple, Amazon, and other providers. Comparative analysis with data-driven pipelines such as Unicom and Mask LID demonstrates SFMS-ALR's flexibility, interpretability, and immediate deployability. The framework establishes a modular baseline for high-quality, engine-independent multilingual TTS and outlines evaluation strategies for intelligibility, naturalness, and user preference.
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。
