The science of emotion
Explore our publications, models, and datasets pushing the boundaries of empathic AI.
in naturalness and expressivity
of emotions and voice characteristics detected
speech LLM latency
Performance
State-of-the-art results
Our models consistently achieve top performance across industry benchmarks.
Most natural voice conversations
In blind comparisons, users consistently rate Hume voices as more natural and human-like than alternatives.
- •Authentic speech rhythms and pauses
- •Natural intonation patterns
- •Human-like breathing and cadence
Superior emotional understanding
Hume's empathic AI demonstrates significantly higher emotional awareness and appropriate responses in conversations.
- •Recognizes frustration and responds with patience
- •Detects excitement and matches energy
- •Senses uncertainty and offers reassurance
Most expressive voice AI
Hume voices convey a wider range of emotions and nuanced expressions compared to other voice AI providers.
- •Warm enthusiasm for good news
- •Gentle concern when discussing problems
- •Playful humor in casual moments
Best pronunciation of challenging content
Our TTS excels at pronouncing difficult content like phone numbers and mathematical expressions that trip up other systems.
- The local mycologist explained that consuming just one fourth plus one fourth equals one half ounce of the misidentified death caps could prove fatal within forty eight hours.
- Most businesses close in the late afternoon from between two until four thirty or five o'clock when it can get hot.
- On december fifteenth two thousand seven, Dennis Kucinich raised one hundred thirty one thousand four hundred dollars from approximately one thousand six hundred donors.
Tested on 2,167 samples
Most accurate emotion identification
When listeners rate how well they can identify the intended emotion, Hume voices consistently outperform competitors.
- •Joy, sadness, anger, fear, surprise
- •Subtle cues like hesitation or relief
- •Complex emotions like bittersweet nostalgia
Identification score (higher is better)
Precisely follows your vocal directions
When you ask for a specific vocal style, emotion, or character, Hume delivers exactly what you requested.
- •"Speak with a whisper, like sharing a secret"
- •"Sound excited and out of breath"
- •"Use a sarcastic, know-it-all tone"
Tested across 32 vocal instructions
Performance
State-of-the-art results
Our models consistently achieve top performance across industry benchmarks. Every claim is backed by rigorous evaluation and reproducible methodology.
Most natural voice conversations
In blind comparisons, users consistently rate Hume voices as more natural and human-like than alternatives.
- •Authentic speech rhythms and pauses
- •Natural intonation patterns
- •Human-like breathing and cadence
Superior emotional understanding
Hume's empathic AI demonstrates significantly higher emotional awareness and appropriate responses in conversations.
- •Recognizes frustration and responds with patience
- •Detects excitement and matches energy
- •Senses uncertainty and offers reassurance
Most expressive voice AI
Hume voices convey a wider range of emotions and nuanced expressions compared to other voice AI providers.
- •Warm enthusiasm for good news
- •Gentle concern when discussing problems
- •Playful humor in casual moments
Best pronunciation of challenging content
Our TTS excels at pronouncing difficult content like phone numbers and mathematical expressions that trip up other systems.
- The local mycologist explained that consuming just one fourth plus one fourth equals one half ounce of the misidentified death caps could prove fatal within forty eight hours.
- Most businesses close in the late afternoon from between two until four thirty or five o'clock when it can get hot.
- On december fifteenth two thousand seven, Dennis Kucinich raised one hundred thirty one thousand four hundred dollars from approximately one thousand six hundred donors.
Most accurate emotion identification
When listeners rate how well they can identify the intended emotion, Hume voices consistently outperform competitors.
- •Joy, sadness, anger, fear, surprise
- •Subtle cues like hesitation or relief
- •Complex emotions like bittersweet nostalgia
Precisely follows your vocal directions
When you ask for a specific vocal style, emotion, or character, Hume delivers exactly what you requested.
- •"Speak with a whisper, like sharing a secret"
- •"Sound excited and out of breath"
- •"Use a sarcastic, know-it-all tone"
Recent Publications
Peer-reviewed insights
TADA: A Generative Framework for Speech Modeling via Text-Acoustic Dual Alignment (Under Review)
Modern Text-to-Speech (TTS) systems increasingly leverage Large Language Model (LLM) architectures to achieve scalable, high-fidelity, zero-shot generation. However, these systems typically rely on fixed-frame-rate acoustic tokenization, resulting in speech sequences that are significantly longer than, and asynchronous with their corresponding text. Beyond computational inefficiency, this sequence length disparity often triggers hallucinations in TTS and amplifies the modality gap in spoken language modeling (SLM). In this paper, we propose a novel tokenization scheme that establishes one-to-one synchronization between continuous acoustic features and text tokens, enabling unified, single-stream modeling within an LLM. We demonstrate that these synchronous tokens maintain high-fidelity audio reconstruction and can be effectively modeled in a latent space by a large language model with a flow matching head. Moreover, the ability to seamlessly toggle speech modality within the context enables text-only guidance--a technique that blends logits from text-only and text-speech modes to flexibly bridge the gap toward text-only LLM intelligence. Experimental results indicate that our approach achieves performance competitive with state-of-the-art TTS and SLM systems while virtually eliminating content hallucinations and preserving linguistic integrity, all at a significantly reduced inference cost.
How emotion is experienced and expressed in multiple cultures: a large-scale experiment across North America, Europe, and Japan
Core to understanding emotion are subjective experiences and their expression in facial behavior. Past studies have largely focused on six emotions and prototypical facial poses, reflecting limitations in scale and narrow assumptions about the variety of emotions and their patterns of expression.
Deep learning reveals what facial expressions mean to people in different cultures
Cross-cultural studies of the meaning of facial expressions have largely focused on judgments of small sets of stereotypical images by small numbers of people. Here, we used large-scale data collection and machine learning to map what facial expressions convey in six countries.
Everything your model needs
Why Our Datasets
World-class data for pre-training and fine-tuning your emotion AI models, backed by years of scientific research.
Ethically Sourced
All data collected with informed consent and rigorous privacy protections.
Globally Diverse
Representative samples across cultures, ages, genders, and demographics.
Expert Annotated
Labeled by trained researchers using validated scientific frameworks.
Research Ready
Clean, structured formats optimized for modern ML pipelines.
Explore Our Data
See the structure behind the emotion science
From voice AI training data to multimodal expression datasets, explore our full data catalog.
Browse all datasetsScientific Foundation
Built on decades of research
Our datasets are grounded in peer-reviewed emotion science, developed in collaboration with leading researchers in psychology, affective computing, and machine learning.
Peer-reviewed methods
Built on 53+ publications in affective science and validated by independent researchers.
Neuroscience-informed
Datasets designed around how the brain actually processes and expresses emotion.
Validated accuracy
Benchmarked against gold-standard datasets with documented performance metrics.
Continuous updates
Regularly refined with new research findings and expanded training data.
Dataset Validation
Proven in production
Niantic Spatial × Hume AI: Creating Interactive & Spatially Aware AI Companions
In partnership with Snap Inc. (hardware) and Hume AI (voice), Niantic Spatial has developed location-aware companions for Spectacles, blending Snap Inc.’s AR glasses, Niantic Spatial’s Large Geospatial Model, and Hume’s Empathic Voice Interface (EVI) for natural, emotionally intelligent conversation. Niantic Spatial, the team pioneering AI that understands the physical world, is showcasing a compelling glimpse of what can happen when spatial intelligence and augmented reality meet.
GAF Powers Professional Training with Hume’s Text-to-Speech
To support their extensive training programs and marketing initiatives, GAF leverages Hume's text-to-speech technology to make internal training videos and marketing voiceovers. Our partnership addresses several key needs: Professional training content: Delivering consistent, high-quality audio for thousands of contractors and employees. Marketing collateral: Producing engaging voiceovers for promotional content and product demonstrations. Scalable production: Generating content without the logistics and cost of traditional voice recording. Hume's voice design also proved ideal for GAF. The platform's natural, expressive voices maintain the authoritative yet approachable tone that GAF needs to communicate with contractors, retailers, and customers. Unlike synthetic voices that can sound robotic or overly casual, Hume's TTS technology delivers the polished, trustworthy quality expected from an industry leader.
Hume AI powers conversational learning with Coconote
While traditional note-taking apps require students to manually scroll and search through content, Coconote is creating interactive study experiences through conversational AI. Coconote’s voice chat feature, powered by Hume's EVI, helps users transform static notes into dynamic conversations. Students can: Ask natural questions about their lecture content Receive contextual explanations referencing specific notes, and Engage in quiz-style conversations for active learning—all through natural voice interaction.
Research Areas
Where Hume enables research
From fundamental affective computing to applied behavioral research, our datasets power studies across the full spectrum of emotion science.
Affective Computing
Study how AI systems can recognize, interpret, and respond to human emotions across modalities.
Human-AI Interaction
Research the dynamics of emotional exchange between humans and AI systems.
Psychology & Behavior
Use expression analysis to study human behavior, mental health, and psychological phenomena.
Speech & Language
Analyze prosodic features, sentiment, and emotional expression in human communication.
Multimodal Learning
Explore how emotion manifests simultaneously across face, voice, and language.
Ethics & AI Safety
Study the ethical implications of emotionally-aware AI systems and develop guidelines.
From the Blog
Latest research updates
Introducing the ACII 2026 Dyadic Contest (DaiKon) Workshop & Challenge
Apr 9, 2026
Opensourcing TADA: Fast, Reliable Speech Generation Through Text-Acoustic Synchronization
TADA (Text-Acoustic Dual Alignment) is Hume AI's open-source speech-language model that synchronizes text and audio one-to-one.
Mar 10, 2026
Introducing OCTAVE (Omni-Capable Text and Voice Engine)
A frontier speech-language model with new emergent capabilities, like on-the-fly AI voice and personality creation.
Dec 23, 2024
License our datasets
Access world-class expression datasets and collaborate with our team on advancing emotion AI.