Human Feedback API
Hume AI's Human Feedback API is a platform designed to embed emotional intelligence into voice models by providing open source models, datasets, and evaluation APIs.
Publisher review
Hume AI's Human Feedback API is a platform designed to embed emotional intelligence into voice models by providing open source models, datasets, and evaluation APIs. It targets developers, researchers, and product teams building conversational AI, virtual assistants, or empathic voice interfaces who need reliable human preference data to improve their models. The API enables systematic collection of human feedback on voice outputs, focusing on dimensions like listenability, audio quality, and smoothness, using science-backed survey templates. This tool is part of Hume's broader ecosystem, which includes the EVI 3 speech-language model, and is positioned for those who prioritize emotional alignment and user experience in voice AI applications.
The Human Feedback API works through a RESTful interface that allows users to create and manage evaluation studies programmatically. It draws from a worldwide pool of reliable, vetted participants to deliver unbiased human preference data, with turnaround times measured in hours rather than weeks. The platform supports benchmarking voice models against industry standards, providing comparative scores across models like Gemini 2.5 Pro, Elevenlabs v3, and OpenAI GPT-4o-TTS. Hume's research spans over 50 languages, 48+ emotions, and 600+ voice descriptors, underpinning the evaluation templates. The API integrates with Hume's EVI 3, which can speak expressively with any voice—real or designed—without fine-tuning, and supports voice cloning from just 30 seconds of audio.
In the market for human evaluation tools for voice AI, the Human Feedback API competes with platforms like Phoenix-4 and Tavus, though it differentiates through its focus on emotional intelligence and empathic voice interfaces. Unlike automated metrics, it provides human-centered feedback on subjective qualities such as emotional resonance and naturalness. Hume's EVI 3 is the first speech-language model that generates language and speech with the same intelligence, offering faster and higher-quality conversational AI compared to traditional TTS systems. The API's benchmarking capabilities allow direct comparison with leading models, making it a specialized tool for teams iterating on voice model quality.
Trade-offs include reports that some users found the interpretations generic, particularly for nuanced emotional expressions. Non-English outputs may sound weaker according to some users, potentially limiting global applicability. The API lacks a simple audio export option, which could complicate workflows for teams needing to download evaluation samples. Additionally, the pricing, starting at $0.0X per minute, may scale for large applications but could be a barrier for small-scale projects. The platform's reliance on a vetted participant pool ensures quality but may introduce latency for niche languages or demographics.
How it works
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RESTful API for studies
Programmatically create and manage evaluation studies using simple endpoints, enabling automated workflows for human feedback collection.
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Vetted participant pool
Access a worldwide pool of reliable, vetted participants to obtain unbiased human preference data on voice model outputs.
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Fast data turnaround
Receive human preference data in hours rather than weeks, accelerating iterative model improvement cycles.
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Science-backed survey templates
Use templates designed for listenability, audio quality, and smoothness to capture scientifically grounded preference feedback.
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Benchmarking against standards
Compare voice models against industry standards, with scores for models like Gemini 2.5 Pro and Elevenlabs v3.
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Open source models and datasets
Access open source models and datasets for training and evaluating emotional intelligence in voice AI systems.
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Emotional intelligence focus
Embed emotional intelligence into voice models using research spanning 50+ languages, 48+ emotions, and 600+ voice descriptors.
Strengths and trade-offs
Strengths
- High-quality ratings from reliable, vetted participants ensure unbiased human feedback for voice model evaluation.
- Fast turnaround delivers human preference data in hours, enabling rapid iteration compared to traditional weeks-long studies.
- Benchmarking against industry standards provides comparative scores across models like Gemini 2.5 Pro and OpenAI GPT-4o-TTS.
- Supports empathic voice interfaces with science-backed templates focused on listenability, audio quality, and smoothness.
Trade-offs
- Some users found interpretations generic, particularly for nuanced emotional expressions in voice outputs.
- Non-English outputs sound weaker according to some users, potentially limiting effectiveness for global applications.
- Missing a simple audio export option, complicating workflows that require downloading evaluation samples for offline analysis.
- Pricing starting at $0.0X per minute may be a barrier for small-scale projects or teams with limited budgets.
Pricing context
Starting at $0.0X per minute, with options for lower rates well below $0.02 per minute for large-scale applications; contact sales for custom pricing.
Getting started with Human Feedback API
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Sign up for API access
Go to the Hume AI website and create an account. Complete the registration process to obtain your API key, which you will use to authenticate requests to the Human Feedback API.
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Connect your voice model
Use your API key to authenticate and connect your voice model to the Human Feedback API. Provide the model endpoint or audio samples you want evaluated, ensuring the API can access your outputs for feedback collection.
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Configure evaluation study
Create an evaluation study via the RESTful API by specifying parameters such as the survey template (e.g., listenability, audio quality) and the number of participants. Define the dimensions you want to measure, like smoothness or emotional resonance.
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Run a feedback collection
Submit your voice model outputs to the study and trigger the feedback collection. The API will distribute your samples to its vetted participant pool and return human preference data within hours.
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Review and iterate on results
Access the collected feedback scores and comparative benchmarks against models like Gemini 2.5 Pro. Use the insights to refine your voice model, then repeat the process to improve emotional alignment and user experience.
Frequently Asked Questions
What is the Human Feedback API from Hume AI?
The Human Feedback API is a platform for embedding emotional intelligence into voice models. It provides open source models, datasets, and evaluation APIs to collect human preference data on voice outputs like listenability and audio quality, targeting developers and researchers.
How does the Human Feedback API collect human preference data?
It uses a RESTful interface to create evaluation studies programmatically, drawing from a worldwide pool of vetted participants. Science-backed survey templates focus on dimensions like listenability, audio quality, and smoothness, delivering unbiased data in hours rather than weeks.
Can I benchmark my voice model against others with this API?
Yes, the API supports benchmarking voice models against industry standards, providing comparative scores for models like Gemini 2.5 Pro, Elevenlabs v3, and OpenAI GPT-4o-TTS. This helps teams iterate on voice model quality using human-centered feedback.
What are the pricing and limitations of the Human Feedback API?
Pricing starts at $0.0X per minute, with lower rates below $0.02 per minute for large-scale applications. Limitations include generic interpretations for nuanced emotions, weaker non-English outputs, no simple audio export, and potential cost barriers for small projects.
How does the Human Feedback API support emotional intelligence in voice AI?
It embeds emotional intelligence through research spanning over 50 languages, 48 emotions, and 600 voice descriptors. The API uses science-backed templates for feedback on emotional resonance and naturalness, integrating with Hume's EVI 3 speech-language model for expressive voice generation.
What are the key features of the Human Feedback API for developers?
Key features include a RESTful API for managing studies, a vetted participant pool for unbiased data, fast turnaround in hours, science-backed survey templates, benchmarking against standards, and access to open source models and datasets for emotional intelligence training.
Alternatives
How Human Feedback API compares
Direct head-to-head against 3 competitors. Picked by 7wData.
Human Feedback API
- Pricing
- Starting at $0.0X per minute, with options for lower rates well below $0.02 per minute for large-scale applications; contact sales for custom pricing.
- Target
- Hume AI's Human Feedback API is a platform designed to embed emotional intelligence into voice models by providing open source models, datasets, and evaluation APIs.
- Strength
- High-quality ratings from reliable, vetted participants ensure unbiased human feedback for voice model evaluation.
- Watch for
- Some users found interpretations generic, particularly for nuanced emotional expressions in voice outputs.
MLflow
- Pricing
- Open source, Databricks hosting at $0.40/DBU
- Target
- ML teams building GenAI apps on Databricks
- Deployment
- Self-hosted or Databricks
- Strength
- Native integration with Databricks and MLflow tracing for GenAI
- Watch for
- Tight coupling to Databricks ecosystem; limited standalone use
Label Studio
- Pricing
- Open source, Cloud from $99/user/month
- Target
- Data labeling teams for AI model training
- Deployment
- Self-hosted or cloud
- Strength
- Mature open-source data labeling platform with broad annotation types
- Watch for
- Complex setup for custom feedback workflows; not GenAI-specific
Log10
- Pricing
- Custom/Contact sales
- Target
- Developers scaling LLM evaluation with custom models
- Deployment
- Cloud API
- Strength
- AutoFeedback system reduces human annotation cost by 10x+
- Watch for
- Early-stage startup; limited public pricing and documentation
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Sources
Reporting on this tool draws on these publicly available sources.