The best speech emotion recognition APIs in 2026

1. 01oruk Speech API

   77.6% measured

   Highest measured accuracy, calibrated outputs

   15 calibrated multilabel emotion labels plus 16 speaking styles, transcription, and unified analysis from one synchronous REST call. English prerecorded audio, priced per second (emotion from $0.0060/min), $5 trial credit. The caveat we state everywhere: the oruk benchmark entry is trained in-distribution while other systems are zero-shot.

2. 02emotion2vec+ (self-hosted)

   68.7% measured

   Best open-weights option

   The strongest non-oruk system measured, and free apart from compute. You take on GPU serving, calibration, thresholding, and scaling — the right trade for ML-mature teams with steady volume.

3. 03Gemini 3 Flash Preview

   46.0% measured

   Best frontier multimodal API

   Prompt an audio-capable LLM and parse the answer. Flexible and convenient if you already run Gemini, but outputs are uncalibrated text, accuracy trails specialist models, and results can shift between model versions.

4. 04Behavioral Signals

   44.1% measured

   Call-center analytics suites

   A speech-analytics vendor whose emotion API measured strongest among the traditional SER vendors. Aimed at contact-center deployments rather than general developer self-serve.

5. 05GPT-Audio 1.5

   43.3% measured

   OpenAI-stack teams

   Same pattern as Gemini: prompt-based emotion judgments from a general audio LLM. Convenient inside an existing OpenAI workflow; not calibrated, and mid-pack on accuracy.

6. 06audEERING devAIce

   42.2% measured

   On-prem and embedded requirements

   From the makers of openSMILE, with SDK and on-premise options that pure cloud APIs lack. Measured accuracy trails the leaders, but deployment flexibility is the differentiator.

FAQ

How was this list ranked?

By measured seven-class accuracy on speech-emotion-bench (July 2026 report): 64,384 held-out clips, one identical scoring pipeline for 64 systems, closed APIs on a fixed 5,000-clip stratified subsample. This is a vendor-published list — oruk builds the #1 entry — so the full results and protocol are downloadable for independent checking.

What happened to Hume AI on this list?

Hume discontinued its Expression Measurement API on June 14, 2026, so it is no longer an option for new integrations. Its prosody model measured 49.6% before the sunset. Hume now focuses on voice agents (EVI 3) and TTS (Octave).

Are LLM APIs like Gemini or GPT-Audio good enough for emotion detection?

For casual use, sometimes. Measured on the same pipeline, frontier multimodal APIs scored 40–46% seven-class accuracy vs 68.7% for the best open specialist model — and their outputs are free-text rather than calibrated scores, which makes thresholding and monitoring harder in production.

What should I check before picking an emotion API?

Four things: published, reproducible accuracy (not demo videos); calibration (are scores decision-ready?); deployment fit (file vs streaming, cloud vs on-prem, language coverage); and honest scope (no vendor can read minds — outputs describe how speech sounds).