# Speaker conditioned acoustic-to-articulatory inversion using x-vectors

arXiv: https://arxiv.org/abs/2006.11536 · HTML: https://mlsanity.com/paper/2006.11536/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2020.

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

- [Acoustic-to-articulatory inversion for dysarthric speech: Are pre-trained self-supervised representations favorable?](https://mlsanity.com/paper/2309.01108/) (2023)

## Abstract (arXiv)

Speech production involves the movement of various articulators, including tongue, jaw, and lips. Estimating the movement of the articulators from the acoustics of speech is known as acoustic-to-articulatory inversion (AAI). Recently, it has been shown that instead of training AAI in a speaker specific manner, pooling the acoustic-articulatory data from multiple speakers is beneficial. Further, additional conditioning with speaker specific information by one-hot encoding at the input of AAI along with acoustic features benefits the AAI performance in a closed-set speaker train and test condition. In this work, we carry out an experimental study on the benefit of using x-vectors for providing speaker specific information to condition AAI. Experiments with 30 speakers have shown that the AAI performance benefits from the use of x-vectors in a closed set seen speaker condition. Further, x-vectors also generalizes well for unseen speaker evaluation.