# The Secret Source : Incorporating Source Features to Improve Acoustic-to-Articulatory Speech Inversion

arXiv: https://arxiv.org/abs/2210.16450 · HTML: https://mlsanity.com/paper/2210.16450/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2022.

## Built on by (most cited first, 5 of 5)

- [Coding Speech through Vocal Tract Kinematics](https://mlsanity.com/paper/2406.12998/) (2024)
- [Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variables](https://mlsanity.com/paper/2309.09220/) (2023)
- [RT-VC: Real-Time Zero-Shot Voice Conversion with Speech Articulatory Coding](https://mlsanity.com/paper/2506.10289/) (2025)
- [Speaker- and Text-Independent Estimation of Articulatory Movements and Phoneme Alignments from Speech](https://mlsanity.com/paper/2407.03132/) (2024)
- [Speech Emotion Recognition with Phonation Excitation Information and Articulatory Kinematics](https://mlsanity.com/paper/2511.07955/) (2025)

## Abstract (arXiv)

In this work, we incorporated acoustically derived source features, aperiodicity, periodicity and pitch as additional targets to an acoustic-to-articulatory speech inversion (SI) system. We also propose a Temporal Convolution based SI system, which uses auditory spectrograms as the input speech representation, to learn long-range dependencies and complex interactions between the source and vocal tract, to improve the SI task. The experiments are conducted with both the Wisconsin X-ray microbeam (XRMB) and Haskins Production Rate Comparison (HPRC) datasets, with comparisons done with respect to three baseline SI model architectures. The proposed SI system with the HPRC dataset gains an improvement of close to 28% when the source features are used as additional targets. The same SI system outperforms the current best performing SI models by around 9% on the XRMB dataset.