Hume AI - Human Feedback for Voice, Speech, and Conversational AI | Hume AI

The data and evaluation layer for emotionally intelligent voice AI

Build & measure AI the way people experience it, based on research and grounded in real human judgment

Our decades of research in multimodal emotional intelligence span

Real World VoiceEQ: a benchmark for measuring the human quality of voice AI

What it is

Four products. One scientific foundation.

01 · Build

The data your model actually needs.
Custom data collection built around your specific scenarios, use cases, and evaluation requirements.
Data Solutions

02 · Simulate + Evaluate

Auto-generate scenarios. Run them. See what breaks.
Kairos lets voice AI teams build evaluation suites from real-world use cases, simulate agent-to-agent and human-to-agent conversations, and track regressions over time, all in record speed.
Kairos Platform

03 · Measure

Identify emotion in voice, in real time.
Real-time emotion tagging and offline analysis across 48+ emotions and 50+ languages with 600+ output metrics, built for voice-native AI teams who need to know what callers are actually feeling.
Expression Measurement API

04 · Rate

Real participants. At scale.
Pre-screened human raters return per-sample scores, free-response feedback, and aggregated analysis. In hours, not days.
Human Feedback API

Leaderboards

The standard for voice AI performance

RW-Voice-EQ Bench

4 Leaderboards
How leading voice AI models perform across recognition, understanding, expression, and conversation, ranked by human judgment.
Explore the benchmark

SLM Judge

1 Leaderboard
Not a model leaderboard, a leaderboard of the judges. Which SLMs track human ratings most closely, so you know which automated evaluator you can actually trust.
View the leaderboard

Why it matters

Voice AI quality is built in layers

A reliable, safe foundation is table stakes. Expressivity and natural flow build on top. And at the peak sits emotional intelligence, the dimension that determines whether people actually want to keep talking to voice AI.

Metrics measure accuracy. Expression, emotion, and alignment require human judgment and the right tools to collect it.

What we evaluate

Built for the questions voice teams actually ask

Does the voice fit the use case, persona, and intended role?

Does the model sustain character voices and emote when reading a story?

Are jokes, reassurances, and facts delivered in distinct tones?

Can the model reliably read out assorted web search results?

Key differentiators

Single API call

From study creation to results, one simple request, no operational burden.

Screening & QA

Sophisticated participant screening, fraud detection, and quality monitoring built in.

Simulation at scale

Agent-to-agent and human-to-agent conversation simulation, built for real-world scenarios.

Hours, not days

Ratings back fast enough to close the human eval loop at model-development pace.

Proven on real models

Used internally to evaluate voice AI across simulation, expression measurement, and human studies.

Custom integrations, evaluations, and question types

Collaborate with us to start running human evaluations on your next models. Our research engineering team is ready to help.