Select models by use-case performance, not lab benchmarks
Voice AI models must be selected, analyzed, and measured against their performance for their intended use case—not based on laboratory-condition benchmarks, because real-life conversations are messy and enterprise-grade voice agents are a hard engineering problem.
Stützende Belege
Cartesia | A Guide to Choosing Voice AI Models
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It is tempting to choose the models based on benchmarks that measure them in laboratory conditions. But real-life conversations are messy, and that makes enterprise-grade voice agents a hard engineering problem. We cannot solve these hard problems by treating voice models as interchangeable commodities. Instead, they must be picked, analysed and measured against their performance for their intended use case .