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.
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 .
Turn detection is critical and distinct from ASR accuracy
Turn detection, also called end pointing, detects when a user has finished their turn. The author describes it as extremely hard without visual or other human conversational cues, and says poor turn detection causes long pauses and high latency.
Turn-detection is also known as end pointing – because it refers to detecting when the user has finished their turn. It is extremely hard to do accurately as Speech to Text models do not have visual or other cues like humans do in conversation. That makes high-quality turn detection incredibly important for conversational AI - poor turn detection results in very long awkward pauses and high latency in the voice agent’s conversation.
Word Error Rate can mislead when the error classes are irrelevant or when a non-streaming ASR model is compared with a streaming model. The author says many benchmarks fail to distinguish streaming and non-streaming use cases.
Word Error Rate (WER) for STT/ASR is a misleading metric if you’re looking at the wrong or irrelevant classes of errors. Or if you’re comparing a non-streaming ASR model (which is inherently easier to be accurate on) with a streaming ASR model. Unfortunately many benchmarks do not distinguish between ASR streaming vs non-streaming use cases.
The Artificial Analysis Intelligence Index scores models on common tasks and reports cost per task, enabling plotting on an intelligence-vs-cost curve; the Pareto frontier identifies models that are simultaneously the cheapest and smartest.
The Artificial Analysis Intelligence Index scores models on a common set of tasks and reports the cost per task, so you can plot them all on one curve of intelligence against cost. The Pareto frontier is the set of models that are the cheapest and smartest.