Every LLM-based text-to-speech system shipping today carries a structural flaw: text tokens and audio frames move at incompatible speeds inside the same model, forcing engineers to choose between reliability, quality, and inference cost. Hume AI's TADA: A Generative Framework for Speech Modeling via Text-Acoustic Dual Alignment eliminates the mismatch entirely — enforcing strict one-to-one synchronization between text tokens and continuous acoustic vectors, producing zero content hallucinations across 1,000+ test samples and running at 5× the throughput of comparable systems.
