Weak-Supervised Dysarthria-invariant Features for Spoken Language Understanding using an FHVAE and Adversarial Training

TL;DR

This paper introduces a novel weakly supervised approach combining FHVAE and adversarial training to extract dysarthria-invariant speech features, improving spoken language understanding accuracy for dysarthric speech, especially for unseen speakers with severe dysarthria.

Contribution

It proposes a new method integrating adversarial training with FHVAE to better disentangle content and speaker features in dysarthric speech, enhancing model generalization.

Findings

Higher accuracy on unseen speakers with severe dysarthria.

Improved feature disentanglement for dysarthric speech.

Enhanced speaker invariance in speech features.

Abstract

The scarcity of training data and the large speaker variation in dysarthric speech lead to poor accuracy and poor speaker generalization of spoken language understanding systems for dysarthric speech. Through work on the speech features, we focus on improving the model generalization ability with limited dysarthric data. Factorized Hierarchical Variational Auto-Encoders (FHVAE) trained unsupervisedly have shown their advantage in disentangling content and speaker representations. Earlier work showed that the dysarthria shows in both feature vectors. Here, we add adversarial training to bridge the gap between the control and dysarthric speech data domains. We extract dysarthric and speaker invariant features using weak supervision. The extracted features are evaluated on a Spoken Language Understanding task and yield a higher accuracy on unseen speakers with more severe dysarthria compared to features from the basic FHVAE model or plain filterbanks.