Hume AI releases the Hume Decision API, using an audio-native model to answer questions about recordings
Hume AI launches the Hume Decision API, an audio-native model that can directly analyze the recording itself to answer questions about the recording. Users can customize questions and options, for example, determining whether a caller appears frustrated, and obtain an estimate of how human listeners would answer. In preliminary tests, the model's agreement rate with the review panel's average answers was 70.7%, compared with 56.5% for an individual human listener.
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The original text provides comparison data against the average answers of human listeners, which readers can use to judge where the audio-native model stands on subjective judgment tasks.
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View original postIntroducing the Hume Audio Decision API. Ask anything: How many speakers? Is anyone laughing? Is the caller frustrated? What used to take 10+ models is now one call. Trained on 10M+ human judgments, so it answers the way human listeners would. Realtime, or across hours of recordings. Request access below.
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Introducing the Hume Audio Decision API. Ask anything: How many speakers? Is anyone laughing? Is the caller frustrated? What used to take 10+ models is now one call. Trained on 10M+ human judgments, so it answers the way human listeners would. Realtime, or across hours of recordings. Request access below.
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Hume AI has launched a new Hume Decision API, an audio-native model that can answer questions about a recording by analyzing the audio itself.
Users can write their own questions and answer options, such as whether a caller sounds frustrated, and receive an estimate of how human listeners would answer.
In initial tests, the model agreed with the panel's average answer 70.7% of the time, compared to 56.5% for a single listener.