Streaming TTS Latency Tradeoff: Real-Time Accuracy Loss 2026

Streaming Text-to-Speech (TTS) suffers from accuracy issues compared to batch processing due to its architectural constraints, which provide 5-20 times less context. This results in premature phonetic decisions that negatively impact the pronunciation of alphanumeric IDs, phone numbers, and addresses, especially under concurrent loads where GPU contention further degrades performance. Non-autoregressive architectures, while offering lower synthesis latency, are limited by neural TTS concurrency caps imposed by cloud providers, which exacerbate context limitations. In scenarios where precise pronunciation is crucial, such as contact centers handling complex entity types, batch processing is favored because it can analyze complete inputs before synthesis, accommodating longer latency. Hybrid architectures that dynamically route content based on complexity and latency requirements are recommended to balance user experience and infrastructure cost. These systems must be evaluated under realistic load conditions to understand the trade-offs between streaming and batch TTS, using benchmarks that account for entity-specific accuracy and tail latency.