Multimodal & Cultural-Aware Emotion Recognition
Consumer mobile app for real-time emotion detection. 8 emotion categories, 99 languages, 7 cultural profiles, 4 modalities (face, speech, voice and posture/gesture). All processing on-device — privacy first.
Try Live Demo →Multimodal & cultural-aware emotion recognition — AI detection of emotions from face, speech, voice and posture/gesture.
EmoSphere captures emotional signals across face, voice, and text for comprehensive understanding.
Real-time facial expression analysis using Vision Transformers. Detects micro-expressions and maps to 8 emotion categories with high accuracy across diverse demographics.
Speech emotion recognition powered by Wav2Vec2. Analyzes prosody, pitch, energy, and temporal patterns to identify emotional states from audio signals.
Sentiment and emotion analysis from text input using DistilRoBERTa. Understands nuanced language, context, and emotional undertones in real-time.
A balanced emotion space designed for real-world consumer applications.
Emotion expression varies across cultures. EmoSphere adapts its recognition models to 7 cultural profiles.
| Cultural Profile | Region | Expression Style | Key Adaptation |
|---|---|---|---|
| Western | North America, Western Europe | Expressive | High facial weight |
| East Asian | China, Japan, Korea | Restrained | Higher voice/text weight |
| South Asian | India, Southeast Asia | Expressive with nuance | Balanced modalities |
| Middle Eastern | Arab states, Iran | Context-dependent | Text emphasis |
| African | Sub-Saharan Africa | Community-oriented | Voice prosody focus |
| Latin American | Central & South America | Highly expressive | Face + voice priority |
| Neutral/Global | Cosmopolitan contexts | Mixed | Default balanced weights |
Visual modality carries the highest default weight, reflecting the richness of facial expression data in emotion recognition.
Audio features including prosody, pitch contour, and energy patterns provide strong emotional signals, especially for arousal.
Linguistic content provides semantic context and emotional nuance. Weights shift culturally where text expression dominates.
Default weights (45/35/20) adapt based on cultural profile, modality availability, and confidence scores. When a modality is unavailable or low-confidence, weights redistribute automatically to maintain accuracy.
All emotion processing happens entirely on-device. No audio, video, or text data ever leaves the user's phone. Zero cloud dependency for inference.
Available on Android and iOS with a consistent experience. Built with React Native and on-device ML runtimes for native performance.
Optimized for mobile hardware with quantized models and efficient inference pipelines. Sub-second emotion detection on modern devices.
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