# Hume launches the first decision model for voice AI > Source: > Published: 2026-11-02 17:07:10+00:00 # Decision models for voice AI 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, pauses, 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. 1. **Hume’s Decision Model** Hume82% 2. **Gemini 3.8 Flash** Google78% 3. **Qwen3.8-Omni-Flash** Alibaba76% 4. **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.