Speech-to-Speech vs Cascaded Voice AI: Which Architecture Should You Deploy?
Blog post from Coval
Speech-to-speech (S2S) voice AI models offer significant latency reduction and enhanced emotional expression by processing audio directly without a text intermediary, unlike traditional cascaded systems that link speech-to-text, language processing, and text-to-speech steps. Despite S2S's promise of faster and more natural outputs, enterprise adoption remains limited due to challenges in control, debuggability, and compliance that cascaded architectures handle more effectively. Enterprises prioritize resolution rates, cost savings, and compliance over conversational naturalness, and thus cascaded systems continue to dominate due to their ability to filter content, offer component-level fallbacks, and provide mature evaluation tools. The anticipated broader adoption of S2S hinges on the maturation of audio-native evaluation tools, improved debugging capabilities, and adapted compliance frameworks, with predictions indicating a rise in S2S deployment for specific use cases, such as premium customer support, by the end of 2026.
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