The bar is human. Hume diagnoses system breaks and feeds failures into improvement.
A sample telehealth call plays while three questions about it are answered for every five seconds of audio.
Why audio needs a decision model #
Voice AI considers tone, s, and sounds. Developers set Q&A; the audio decision model gives probabilities for quick audio judgments.
Different questions. The same audio. #
Ask objective, subjective or perceptual questions about speech and sound. Select a tab to explore examples.
One speaker
Two speakers
Three or more speakers
Find the audio worth acting on #
Ask more of the same audio
Apply several questions to the same recordings, from naturalness and clarity to empathy and timing.
Designed for fast evaluation
Repeat evaluations across your collection as your model or criteria change.
Pinpoint cases for your next step
Find candidate examples for training, isolate evaluation failures, and surface cases for escalation review.
More reliable than #
a single listener
The Audio Decision Model agrees with the human crowd 70.7% of the time, while a single human rater agrees 56.5% of the time.
- Number of client hours processed.
- 1B hours
- Human judgments across Hume’s work.
- 10M+
Agreement with a panel of human raters: Audio Decision Model 70.7%, Individual Rater 56.5%.
Accuracy against speed: Hume’s Decision Model averages 0.82 over 10 leading benchmarks at 0.23 seconds median latency per screen, scoring higher and answering faster than Gemini 3.8 Flash, Qwen3.8-Omni-Flash, Inkling-Small, gpt-audio-1.5, Gemini 3.5 Flash Lite and Nemotron 3 Nano Omni.
The Audio Decision Model answers audio questions faster and more accurately than any other model on the market.
- Average score over 10 leading benchmarks: MMAU, ParaPair, EmphAssess, etc.
- 0.82
- Median latency per screen
- 0.23 s
Compare the judgments that matter #
Accuracy across the criteria you care about.
- Hume’s Decision Model Hume82%
- Gemini 3.8 Flash Google78%
- Qwen3.8-Omni-Flash Alibaba76%
- Inkling-Small Thinking Machines67%
Questions #
about the model
It’s Hume’s audio-native decision model. Developers submit audio, a question and possible answers; it returns a probability for each answer. It helps teams evaluate speech and sound, assess how a voice comes across to listeners, and inform decisions in voice applications.
Audio decisions that fit your stack. #
Send audio, your question and possible answers.
The audio decision model returns a probability for each
Use the Decision Model API as a decision layer in a voice-agent system with speech recognition and generation.