Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features

TL;DR

This paper introduces a novel set of acoustic features derived from models trained on healthy speech to improve hypernasality estimation in dysarthria, demonstrating better generalization across different disorders and datasets.

Contribution

The paper proposes a new acoustic feature set based on models trained on healthy speech, enhancing hypernasality detection across various dysarthria types and datasets.

Findings

Features generalize across different diseases and datasets.

Models trained on healthy speech can effectively measure hypernasality.

Improved robustness over previous engineered features.

Abstract

Hypernasality is a common characteristic symptom across many motor-speech disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific speech datasets on which they are trained. Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal speech from one disease and evaluating on hypernasal speech from another disease (e.g. training on Parkinson's disease, evaluation on Huntington's disease), and when training on neurologically disordered speech but evaluating on cleft palate speech.