Beyond Unified Models: A Service-Oriented Approach to Low Latency, Context Aware Phonemization for Real Time TTS
Lightweight, real-time text-to-speech systems are crucial for accessibility. However, the most efficient TTS models often rely on lightweight phonemizers that struggle with context-dependent challenges. In contrast, more advanced phonemizers with a deeper linguistic understanding typically incur high computational costs, which prevents real-time performance.
  This paper examines the trade-off between phonemization quality and inference speed in G2P-aided TTS systems, introducing a practical framework to bridge this gap. We propose lightweight strategies for context-aware phonemization and a service-oriented TTS architecture that executes these modules as independent services. This design decouples heavy context-aware components from the core TTS engine, effectively breaking the latency barrier and enabling real-time use of high-quality phonemization models. Experimental results confirm that the proposed system improves pronunciation soundness and linguistic accuracy while maintaining real-time responsiveness, making it well-suited for offline and end-device TTS applications.
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Mahta Fetrat
claimed_verified
2025-12-11T10:07:53.073Z
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Mahta Fetrat
MahtaFetrat
user
MahtaFetrat
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Donya Navabi
admin_assigned
2025-12-11T10:54:32.567Z
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Donya Navabi
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dninvb
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Zahra Dehghanian
admin_assigned
2025-12-11T10:54:38.768Z
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Dehghanian
ZahraDehghanian97
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ZahraDehghanian97
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Morteza Abolghasemi
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Hamid R. Rabiee
admin_assigned
2025-12-11T10:54:49.039Z
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Hamid R. Rabiee
hrrabiee
user
hrrabiee
Beyond Unified Models: A Service-Oriented Approach to Low Latency, Context Aware Phonemization for Real Time TTS
Lightweight, real-time text-to-speech systems are crucial for accessibility. However, the most efficient TTS models often rely on lightweight phonemizers that struggle with context-dependent challenges. In contrast, more advanced phonemizers with a deeper linguistic understanding typically incur high computational costs, which prevents real-time performance.
  This paper examines the trade-off between phonemization quality and inference speed in G2P-aided TTS systems, introducing a practical framework to bridge this gap. We propose lightweight strategies for context-aware phonemization and a service-oriented TTS architecture that executes these modules as independent services. This design decouples heavy context-aware components from the core TTS engine, effectively breaking the latency barrier and enabling real-time use of high-quality phonemization models. Experimental results confirm that the proposed system improves pronunciation soundness and linguistic accuracy while maintaining real-time responsiveness, making it well-suited for offline and end-device TTS applications.
A framework is proposed to improve phonemization quality in TTS systems without sacrificing real-time performance through lightweight context-aware phonemization and a service-oriented architecture.
G2P-aided TTS phonemization quality inference speed context-aware phonemization service-oriented architecture real-time performance TTS systems pronunciation soundness linguistic accuracy