WeresearchandoptimizeaudiodatasetsforotherfrontiervoiceAIlabs
who are we
We are a frontier voice AI lab
As a frontier research lab, we know what it takes to curate large scale quantities of data that create successful, interactive, and empathic multimodal models. Scaling data is at an inflection point for voice, and we're excited to help labs scale audio pre-training and post-training for speech-language models.
We provide the data and research tooling to help scale model capabilities.
EVERYTHING YOUR MODEL NEEDS
Capabilities
Teach your model to speak 50+ languages, generate voices from prompts, code switch, adopt specific emotions, and more.
Request samples
Explore samples that align with your intended languages, use cases, and model goals.
License access
Scale up access to our large-scale off-the-shelf data, evaluation pipelines, and our voice gym.
Iterate
Collaborate with our researchers to diagnose remaining areas of improvement for your model.
Recent Publications
Peer-reviewed insights
The 2026 ACII Dyadic Conversations (DaiKon) Workshop & Challenge
The 2026 ACII Dyadic Conversations (ACII-DaiKon) Workshop & Challenge introduces a benchmark for modeling interpersonal affect and social dynamics in dyadic conversations. Although conversational affect modeling has advanced rapidly, most benchmarks remain speaker-centric and underrepresent coupled, time-evolving processes between partners, including directional influence, conversational timing coordination, and rapport development. To address this gap, ACII-DaiKon presents three coordinated sub-challenges built on a shared dataset: (1) directional interpersonal influence prediction, (2) turn-taking prediction (next-speaker and time-to-next-speech), and (3) rapport trajectory prediction across full interactions. The challenge is built on the Hume-DaiKon dataset, comprising 945 dyadic conversations (743.4 hours of audiovisual data) collected under naturalistic conditions across five languages. The benchmark supports multimodal modeling, temporal reasoning, and cross-context generalization through fixed train/validation/test splits, standardized metrics, and released baseline systems. Evaluation uses Concordance Correlation Coefficient (CCC), Pearson correlation, Macro-F1, and Mean Absolute Error (MAE) depending on the sub-challenge. Baseline experiments establish initial reference performance, with best test results of 0.40 CCC and 0.50 Pearson for influence prediction, 0.66 Macro-F1 and 1.50~s MAE for turn-taking, and 0.68 CCC and 0.70 Pearson for rapport trajectory modeling. These results indicate that while current methods capture coarse dyadic patterns, robust modeling of directional dependence and long-horizon interpersonal dynamics remains challenging. The workshop provides a shared platform for rigorous comparison and cross-disciplinary discussion on data validity, evaluation protocols, and culturally aware modeling for dyadic interaction.
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.
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.
Research Areas
Where Hume enables research
From fundamental affective computing to applied behavioral research, our tools 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
Disentangling Emotion from Voice: A Cross-Product Sampling Approach for Expressive Voice Data
Humans can separate how they feel from how they speak. Voice models still struggle to do the same.
May 27, 2026
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