arXiv:2606.16327v1 Announce Type: cross Abstract: Recent acoustic-to-articulatory inversion (AAI) models rely on electromagnetic articulography (EMA) data, which are costly and limited in scale. To address this limitation, we propose \textit{ArtBoost}, a novel data augmentation strategy that leverages large-scale speech--mesh datasets originally developed for speech-driven 3D facial animation to improve AAI under limited EMA supervision. \textit{ArtBoost} extracts pseudo articulatory trajectories from visible facial anchors and uses them for pre-training before fine-tuning on real EMA data. Ex
The increasing demand for more advanced and data-hungry AI models in speech and robotics is driving innovation in synthetic data generation and augmentation techniques.
This development could significantly reduce the cost and technical barriers to developing sophisticated AI systems that interpret and generate human-like speech and articulation, expanding their applications.
The ability to generate synthetic articulatory data will accelerate training and improve the accuracy of acoustic-to-articulatory inversion models, making complex speech and facial animation technologies more accessible.
- · AI researchers and developers
- · Robotics and animation industries
- · Healthcare (speech therapy)
- · Metaverse and virtual avatar companies
- · Companies reliant on expensive, manually collected EMA data
- · Less advanced speech AI techniques
Improved performance and broader deployment of acoustic-to-articulatory inversion models.
Faster development and more natural-sounding speech for human-robot interaction and virtual assistants.
Potential for new forms of biometric identification or highly realistic deepfakes based on articulatory patterns.
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Read at arXiv cs.AI