Human Feedback API

One API call.
Real human ratings.

Plug human evaluation into your pipeline. Audio in, structured results out.

API-first · Built for ML pipelines

Plug human eval into your pipeline

Input

Audio files
Questions
Instructions
Metadata
Rater criteria
Study configuration

Output

{
  "study_id": "std_8f3a...",
  "status": "complete",
  "responses": [{
    "sample_id1": {
      "naturalness": {
        "values": [2, 4, 4]
      },
      "voice_match": {
        "values": [4, 3, 4]
      }
    }
  }]
}

Study types

Three core study types

Designed around the questions voice and conversational AI teams actually ask.

Live dialogue · Structured feedback

Live conversations

Real human conversations with AI models. Participants hold live conversations with speech-to-speech systems; structured post-call feedback is collected automatically.

Example evaluation areas
Conversational naturalness
Responsiveness
Emotional intelligence
Voice quality
Helpfulness
Engagement
Model development workflow
  • Test experimental checkpoints with real users
  • Gather rapid human feedback during development
  • Iterate faster on conversational speech systems
  • Benchmark and compare to other voice AI models
One clip · Custom questions

Single-sample rating

Participants evaluate individual audio clips using your custom rating questions.

Example use cases
Transcript accuracy
Voice likability
Emotional expression
Audio quality
Naturalness
Pronunciation
Speaker trustworthiness
Role fit
Also supports
  • Display transcripts, tags, prompts, and speaker info alongside audio
  • Free-response questions for human transcription, qualitative feedback, and error analysis
A
VS
B
Side-by-side preference

Multi-sample comparison

Participants compare audio samples and express preferences across custom dimensions.

Example use cases
Compare TTS models
Compare model checkpoints
A/B test voice quality
Emotional expression
Naturalness comparisons
Style & persona preference
Voice cloning evaluation
Evaluation formats
Preference selectionLikert ratingsRanking tasksSimilarity judgmentsReference sample comparison

Why our ratings are higher quality

High-quality ratings, by design

High-quality participants

Pre-screened raters from multiple trusted pools. Active fraud and AI-response detection.

Speed

Data delivered in hours, not days, closing the human eval loop at the pace your models move.

Unique incentive framework

Performance-based bonuses reward thoughtful, conscientious work, tuned per study and task type.

Fully managed ops

We handle payments, comms, support, and logistics. You never touch a crowd platform.

Global participant network

Many countries, languages, and demographics. Coverage expanding daily.

Subjective metrics

A few good places to start

These are subjective qualities that automated metrics can't capture. They need real human ears and real human judgment.

01 · Role fit

Does this voice sound like your ideal commentator?

Get feedback on how well a voice fits its intended use case: commentator, podcaster, teacher, assistant, and more.

🎤Commentator🎙Podcaster✏️Teacher🤖Assistant☎️Customer support📖Narrator🩺Doctor📢Announcer⚖️Lawyer🎬Actorand more
02 · Listenability

Could you listen to this voice for hours and not get tired of it?

Ask participants how much they would enjoy listening to the voice at length, the kind of judgment only a human can make.

▶ 0:423:188:55

Built by experts in emotion science & voice AI

The same stack behind the world's most human-aligned TTS and speech-to-speech models

Proven in production

Powering evaluation for Hume's own frontier voice models, used daily across training, alignment, and release.

Frontier voice expertise

Built by the team behind Hume's emotionally intelligent voice models, deep expertise in speech quality, prosody, and emotional perception.

Customize or templates

Bring your own questions, or start from the expert-designed templates we use to evaluate our own state-of-the-art models.

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.

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