SFMS-ALR: Script-First Multilingual Speech Synthesis with Adaptive Locale Resolution
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arXiv
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| Format: | Preprint |
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2025
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| author | Donepudi, Dharma Teja |
| author_facet | Donepudi, Dharma Teja |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_25178 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SFMS-ALR: Script-First Multilingual Speech Synthesis with Adaptive Locale Resolution Donepudi, Dharma Teja Sound Artificial Intelligence Audio and Speech Processing I.2.7; H.5.5 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. |
| title | SFMS-ALR: Script-First Multilingual Speech Synthesis with Adaptive Locale Resolution |
| topic | Sound Artificial Intelligence Audio and Speech Processing I.2.7; H.5.5 |
| url | https://arxiv.org/abs/2510.25178 |