# Speaker-Independent Acoustic-to-Articulatory Inversion through Multi-Channel Attention Discriminator

arXiv: https://arxiv.org/abs/2406.17329 · HTML: https://mlsanity.com/paper/2406.17329/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2024.

## Built on by (most cited first, 1 of 1)

- [Seeing Speech: Learning Visible Articulatory Dynamics for Speech-Driven 3D Facial Animation](https://mlsanity.com/paper/2609.30517/) (2026)

## Abstract (arXiv)

We present a novel speaker-independent acoustic-to-articulatory inversion (AAI) model, overcoming the limitations observed in conventional AAI models that rely on acoustic features derived from restricted datasets. To address these challenges, we leverage representations from a pre-trained self-supervised learning (SSL) model to more effectively estimate the global, local, and kinematic pattern information in Electromagnetic Articulography (EMA) signals during the AAI process. We train our model using an adversarial approach and introduce an attention-based Multi-duration phoneme discriminator (MDPD) designed to fully capture the intricate relationship among multi-channel articulatory signals. Our method achieves a Pearson correlation coefficient of 0.847, marking state-of-the-art performance in speaker-independent AAI models. The implementation details and code can be found online.