SFMS-ALR: Script-First Multilingual Speech Synthesis with Adaptive Locale Resolution

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 or 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.