How to Evaluate a Voice Provider: A Field Guide from Cartesia and Coval

Choosing a voice provider for an AI agent should follow a four-stage, evidence-based process rather than relying on short demos or a single ranking. Independent benchmarks can create an initial shortlist using consistent measures such as latency and intelligibility, while blind listening tests help assess naturalness without provider bias. Finalists should then be tested against a fixed set of real-world inputs, including numbers, names, disclosures, languages, and known failure cases, using prewritten objective and subjective acceptance criteria. Each voice must also be evaluated within the full agent system under identical multi-turn scenarios involving speech recognition, turn-taking, tools, telephony, interruptions, noise, and long conversations, since failures may originate outside the voice model. Evaluation should continue after deployment through production monitoring, segmentation of outliers and failures, and conversion of sanitized incidents into regression tests, creating a reusable framework for future provider, model, prompt, or infrastructure changes.

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