CLEAR

The voice AI evaluation framework (CLEAR) is a scorecard for judging how well a voice-based AI is built, checked in a set order. Most marketing for this kind of technology leads with 'the voice sounds human,' but this scorecard pushes that down to third place.

Think of it like judging a dish in a cooking competition. The first thing checked is whether the dish is even edible — that is, whether the AI actually responds properly when someone talks to it. The second is whether it understood the ingredients correctly — that is, whether it accurately grasps what the other person is saying. Only after clearing these two gates does the framework look at the third point: how nicely the dish is plated, or in this case, how natural and human the voice sounds. The fourth and final check is whether that same quality can be reproduced hundreds or thousands of times — that is, whether context and timing stay consistent over long conversations.

Why this order? Because no matter how beautiful the voice sounds, if it gives the wrong answer to a question or fails to understand what's being said, the conversation itself falls apart. The logic behind the framework is that only after conversation flow and comprehension are solidly in place does a natural-sounding voice start to matter for keeping a user's trust over time.

In a video posted on X, Hume AI CEO Andrew Etinger explained CLEAR's evaluation order, saying, "Even if the response and comprehension line up, if the voice doesn't sound natural, you won't want to keep trusting it." Voice AI marketing typically leads with 'how human does the voice sound,' but the key point of this framework is that this comes third, not first.

If you're using a voice AI service, try checking it in this order.

1. Response: Does it give an answer that fits what I said?
2. Comprehension: Does it accurately understand what I said? (Does it avoid asking me to repeat myself or misinterpreting me?)
3. Naturalness: Does the voice sound comfortably human?
4. Consistency: Do the first three hold up the same way over a long conversation or across multiple restarts?

Checking in this order helps catch a common problem early: a demo where the voice sounds impressive but the actual conversation keeps breaking down.