Multilingual Dysarthric Speech Assessment Using Universal Phone Recognition and Language-Specific Phonemic Contrast Modeling

TL;DR

This paper introduces a multilingual dysarthric speech assessment framework that combines universal phone recognition with language-specific phonemic contrast modeling, providing reliable metrics across languages.

Contribution

It presents a novel multilingual assessment method integrating universal phone recognition with contrastive phonological features, enabling cross-language intelligibility evaluation.

Findings

PER benefits from mapping and alignment

PFER benefits from alignment alone

PhonCov benefits from mapping

Abstract

The growing prevalence of neurological disorders associated with dysarthria motivates the need for automated intelligibility assessment methods that are applicalbe across languages. However, most existing approaches are either limited to a single language or fail to capture language-specific factors shaping intelligibility. We present a multilingual phoneme-production assessment framework that integrates universal phone recognition with language-specific phoneme interpretation using contrastive phonological feature distances for phone-to-phoneme mapping and sequence alignment. The framework yields three metrics: phoneme error rate (PER), phonological feature error rate (PFER), and a newly proposed alignment-free measure, phoneme coverage (PhonCov). Analysis on English, Spanish, Italian, and Tamil show that PER benefits from the combination of mapping and alignment, PFER from alignment alone, and PhonCov from mapping. Further analyses demonstrate that the proposed framework captures clinically meaningful patterns of intelligibility degradation consistent with established observations of dysarthric speech.