# Independent and automatic evaluation of acoustic-to-articulatory inversion models

arXiv: https://arxiv.org/abs/1911.06573 · HTML: https://mlsanity.com/paper/1911.06573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Speaker- and Text-Independent Estimation of Articulatory Movements and Phoneme Alignments from Speech](https://mlsanity.com/paper/2407.03132/) (2024)
- [Tracking Articulatory Dynamics in Speech with a Fixed-Weight BiLSTM-CNN Architecture](https://mlsanity.com/paper/2504.18099/) (2025)

## Abstract (arXiv)

Reconstruction of articulatory trajectories from the acoustic speech signal has been proposed for improving speech recognition and text-to-speech synthesis. However, to be useful in these settings, articulatory reconstruction must be speaker independent. Furthermore, as most research focuses on single, small datasets with few speakers, robust articulatory reconstrucion could profit from combining datasets. Standard evaluation measures such as root mean square error and Pearson correlation are inappropriate for evaluating the speaker-independence of models or the usefulness of combining datasets. We present a new evaluation for articulatory reconstruction which is independent of the articulatory data set used for training: the phone discrimination ABX task. We use the ABX measure to evaluate a Bi-LSTM based model trained on 3 datasets (14 speakers), and show that it gives information complementary to the standard measures, and enables us to evaluate the effects of dataset merging, as well as the speaker independence of the model.