Hume AI has released the Hume Decision API, an audio-native model that answers questions about a recording by analyzing the audio itself rather than relying on a transcript.
Hume AI · Hume Decision API
Answering questions from the sound of speech, not the transcript
Developers define their own questions and answer options. The model analyzes the audio directly and estimates how humans would answer, such as whether a caller sounds frustrated.
How the Decision API works
1 · Audio
A recorded conversation, such as a customer call.
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2 · Custom question
The developer sets the question and answer options, for example "Does the caller sound frustrated?"
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3 · Estimated answer
The model estimates how humans would answer each option, based on tone and emotion in the voice.
Two pipelines for analyzing speech
Transcript-first (common today)
Audio →
Transcript →
Text analysis
Cues carried by voice, such as tone and hesitation, can be lost before analysis begins.
Audio-native (Decision API)
Audio →
Answer estimate
The model works on the sound itself, so voice cues can inform the answer directly.
Why it matters
Targets call review, quality monitoring, and conversational research.
Replaces some manual listening, where reviewers typically work through recordings by hand.
Could let developers without in-house machine learning teams build custom audio classification.
What remains unproven
Agreement with human judgments is not yet measured.
Performance across languages, accents, and recording conditions is unknown.
Subjective labels such as "frustrated" raise open questions on consistency and bias.
Key takeaway
Reliability is the deciding factor, and several details are still missing
Accuracy
Not yet independently evaluated.
Pricing and access
No pricing or availability tiers included in the announcement.
Data handling
How recorded calls are stored and processed is still unclear.
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