Hume AI alternatives for emotion measurement

The alternatives, measured

Accuracy is seven-class emotion accuracy on speech-emotion-bench — 64,384 held-out clips, one identical scoring pipeline, closed APIs on a fixed 5,000-clip stratified subsample. Disclosure: this is a vendor page; the full 64-system results and protocol are published and downloadable so you can check our work.

How to choose

oruk Speech API

15 calibrated multilabel emotions + 16 speaking styles + transcription from one synchronous REST call. English, file-based, priced per second. (This is our product; the benchmark entry is trained in-distribution — see methodology.)

emotion2vec+ (self-hosted)

The strongest open-weights option measured. Free to run, but you own GPU serving, calibration, thresholds, and scaling. A good fit for teams with ML infrastructure.

Gemini 3 Flash Preview

The strongest frontier multimodal API measured. Prompt-based rather than calibrated scores; useful when emotion is a secondary need inside a broader LLM workflow.

Behavioral Signals

Speech analytics vendor with an emotion/behavior API aimed at call centers.

GPT-Audio 1.5

OpenAI’s audio-capable model, prompt-based. Same trade-off as Gemini: flexible, not calibrated.

audEERING devAIce

Long-standing paralinguistics vendor (makers of openSMILE); offers on-prem and embedded deployments.

Empath

Japanese vocal-emotion vendor; scored lowest of the commercial APIs measured.

Still need what only Hume EM did — facial expressions, vocal bursts, or streaming measurement? None of the audio-file APIs above replace those modalities; evaluate multimodal vendors directly.

FAQ

Why do most "Hume AI alternatives" lists recommend TTS products?

Because Hume is now primarily a voice-agent and TTS company (EVI 3, Octave), most alternative lists compare voices. This page covers the discontinued product people are actually migrating from — the Expression Measurement API — and alternatives that measure emotion in audio.

Which alternative is most accurate?

On speech-emotion-bench (64 systems, one identical scoring pipeline), oruk Spectra measured 77.6% seven-class accuracy — with the stated caveat that it is trained in-distribution — followed by the open emotion2vec+ family (~68.7%, zero-shot). Among other commercial options, Gemini 3 Flash Preview measured 46.0% and Hume’s own prosody model 49.6% before retirement. Full results are downloadable from the benchmarks page.

Is there a free alternative to Hume Expression Measurement?

Yes — self-hosting an open model such as emotion2vec+ is free apart from compute, and it was the strongest non-oruk system measured. The trade-off is operational: GPU serving, calibration, and maintenance are on you. oruk includes $5 in trial credit if you want a managed comparison point.