Conversational AI is transforming how humans interact with technology. From virtual assistants and AI-powered contact centers to real-time translation and accessibility tools, voice-first interfaces are rapidly becoming the default mode of interaction for millions of users worldwide.

Yet delivering a truly natural voice experience remains one of the hardest challenges in AI infrastructure. Users expect human-like responsiveness. Excess latency can break conversational flow and erode trust. To feel natural, AI-generated speech must arrive within the same temporal window as human turn-taking.

This post introduces how Cartesia, an AWS Partner specializing in real-time voice AI, uses purpose-built State Space Models (SSMs) and AWS infrastructure to deliver sub-100ms text-to-speech and speech-to-text at global scale. Developers can build voice experiences that feel indistinguishable from human conversation.

Traditional transformer-based text-to-speech (TTS) models struggle with streaming latency because they process entire sequences before producing output. This architectural limitation creates a fundamental tension: higher quality often means higher latency. A new class of models, State Space Models, resolves this trade-off.

State Space Models (SSMs) take a fundamentally different approach to processing sequences than transformers, the architecture behind most of today’s AI. Transformers use a mechanism called attention, which compares every element in a sequence to every other element. As a result, the amount of computation grows quadratically as the sequence gets longer. SSMs instead treat a sequence as a system that evolves continuously over time, processing it step by step and carrying a compact internal state forward. This can be converted into a form computers handle efficiently, keeping the cost low for certain long sequences.

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