# Enhancing Acoustic-to-Articulatory Inversion with Multi-Target Pretraining for Low-Resource Settings

Source: [Enhancing Acoustic-to-Articulatory Inversion with Multi-Target Pretraining for Low-Resource Settings](<https://www.sota2.com/research/paper/6a474208143ebdcacacc5bdf>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-07-04T05:01:04.042Z

Authors: Jesuraj Bandekar, Prasanta Kumar Ghosh

Published: 2026-07-02T01:43:57.000Z

## Abstract

Acoustic-to-Articulatory Inversion (AAI) estimates vocal tract articulator movements from speech, benefiting tasks like ASR, speech synthesis, and speaker verification. While deep learning-based methods (CNNs, RNNs, Transformers) have advanced AAI, recent studies show that Self-Supervised Learning (SSL) features further enhance performance, particularly in low-resource settings. However, SSL feature extractors introduce inference latency and computational overhead. To address this, we propose a novel pretraining method leveraging three target representations-Phoneme Labels, Articulatory Feature Labels, and Critical-articulator Labels-eliminating the need for an SSL extractor during inference. We evaluate our approach against both baseline and SSL-based models across various data conditions. Results demonstrate that our method consistently improves AAI performance, particularly in low-resource scenarios, while significantly reducing inference costs without sacrificing accuracy.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Articulatory-to-Acoustic Inversion on EMA seen speakers (test)](<https://www.sota2.com/research/sota/articulatory-to-acoustic-inversion-on-ema-seen-speakers-test.md>): Articulatory-to-Acoustic Inversion; EMA seen speakers (test); CC: 0.8826.

- [Articulatory-to-Acoustic Inversion (AAI) on EMA dataset unseen speakers (test)](<https://www.sota2.com/research/sota/articulatory-to-acoustic-inversion-aai-on-ema-dataset-unseen-speakers-test.md>): Articulatory-to-Acoustic Inversion (AAI); EMA dataset unseen speakers (test); CC: 0.7818.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2607.01594>)

[PDF](<https://arxiv.org/pdf/2607.01594.pdf>)