Revolutionizing Multimodal AI Communication
Unifying Speech & Gesture Synthesis with Interleaved Token Prediction
Gelina introduces an innovative autoregressive framework for synchronized generation of speech and co-speech gestures from text, leveraging interleaved token sequences to achieve enhanced naturalness and expressiveness in AI-driven communication.
Gelina demonstrates significant advancements in multimodal AI communication, as evidenced by objective and subjective evaluations.
2x
Improved Gesture Quality (FGD-B)
15%
Higher Speech Sync (BC)
3.5
Competitive Speech MOS
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Gelina's Unified Generation Process
Text Input
→
Text Tokenization
→
AR Backbone (Interleaved Prediction)
→
Modality-Specific Decoders
→
Synchronized Speech & Gestures
75 Hz
Speech Tokenization Rate (WavTokenizer)
Gelina utilizes WavTokenizer to convert speech waveforms into discrete tokens at a high frequency, ensuring detailed audio representation for synthesis.
| Strategy | Mono-modal Data | Paired Multimodal Data | Benefits |
|---|---|---|---|
| Gelina | Extensive (GigaSpeech, LibriTTS, MLS-10k) | Scarce (BEAT2) - Fine-tuning |
|
| Prior Work (e.g., Diff-TTSG) | Limited/None | Mono-speaker/Synthetic (Trinity) |
|
Leveraging Large-Scale Monomodal Data
Company: Gelina Project
Challenge: The scarcity of large-scale paired speech-gesture datasets (e.g., BEAT2) limited the ability to train robust multimodal AI models that could generalize across various speakers and styles.
Solution: Gelina implemented a two-stage training strategy: first, pre-training the autoregressive backbone on vast unimodal text-speech datasets, then fine-tuning on limited paired text-speech-gesture data. This allowed the model to learn robust text-speech alignment before introducing gesture synchronization.
Result: This approach significantly improved Gelina's ability to generalize across multiple voices and gestural styles, achieving competitive speech quality and superior gesture generation compared to unimodal baselines, without relying on synthetic data augmentation for multi-speaker capabilities.
FGD-B: 0.0839
Gelina Cloning - Lowest Fréchet Gesture Distance (Body)
This metric indicates that Gelina's generated gestures are closest to human distributions, showcasing superior naturalness and fidelity compared to baselines.
3.5
Competitive Speech MOS
Gelina achieves speech quality on par with strong speech-only systems (CosyVoice-2, Lina-Speech), demonstrating its multimodal capabilities do not compromise speech naturalness.
Calculate Your Potential ROI
Estimate the time and cost savings your enterprise could achieve by implementing unified speech and gesture synthesis.
Annual Cost Savings
$0
Annual Hours Reclaimed
0
Your Implementation Roadmap
A phased approach to integrating Gelina's advanced multimodal synthesis capabilities into your enterprise systems.
Phase 1: Discovery & Customization (2-4 Weeks)
Initial assessment of your existing communication platforms and definition of specific requirements for speech and gesture styles. Custom model training for unique brand voices and gestural nuances.
Phase 2: Integration & Testing (4-8 Weeks)
Seamless API integration with your applications, virtual assistants, or digital human interfaces. Comprehensive testing for quality, synchrony, and performance across diverse use cases.
Phase 3: Deployment & Optimization (Ongoing)
Full-scale deployment with continuous monitoring and iterative optimization based on user feedback and performance metrics, ensuring maximum ROI and natural user interactions.
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