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

Illa, Aravind; Ghosh, Prasanta Kumar

doi:10.48550/arxiv.2006.11536

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2022

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“…Combining these two features has also been found to generate more accurate trajectories [18]. A few other works try to combine other modalities like realtime MRI and speaker vectors [19] to improve performance.…”

Section: Introductionmentioning

confidence: 99%

Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning models

Udupa¹,

Siddarth²,

Ghosh³

2022

Preprint

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In this work, we investigate the effectiveness of pretrained Self-Supervised Learning (SSL) features for learning the mapping for acoustic to articulatory inversion (AAI). Signal processing-based acoustic features such as MFCCs have been predominantly used for the AAI task with deep neural networks. With SSL features working well for various other speech tasks such as speech recognition, emotion classification, etc., we experiment with its efficacy for AAI. We train on SSL features with transformer neural networks-based AAI models of 3 different model complexities and compare its performance with MFCCs in subject-specific (SS), pooled and fine-tuned (FT) configurations with data from 10 subjects, and evaluate with correlation coefficient (CC) score on the unseen sentence test set. We find that acoustic feature reconstruction objective-based SSL features such as TERA and DeCoAR work well for AAI, with SS CCs of these SSL features reaching close to the best FT CCs of MFCC. We also find the results consistent across different model sizes.

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Section: Introductionmentioning

confidence: 99%