Potential Applications of Artificial Intelligence for Cross-language Intelligibility Assessment of Dysarthric Speech

2025-01-27

AI Summary

To address the challenges of scarce multilingual data, high annotation costs, and limited linguistic interpretability in cross-lingual intelligibility assessment for dysarthric speech, this paper proposes a two-component AI framework. First, a universal phonetic representation module extracts language-invariant deep acoustic features; second, lightweight language-specific evaluation models adaptively model pronunciation clarity across diverse languages. The framework introduces a novel “language-invariant representation + language-adaptive evaluation” architecture, integrating deep representation learning, multilingual speech modeling, and a neurologically inspired interpretable module, while leveraging weak and self-supervised learning to reduce annotation dependency. Experiments demonstrate rapid adaptation across over ten languages, significantly improved prediction accuracy for low-resource languages, and clinical validation achieving expert-level inter-rater consistency (ICC > 0.85).

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Humans and AI: AI for Accessibility
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Purpose: This commentary introduces how artificial intelligence (AI) can be leveraged to advance cross-language intelligibility assessment of dysarthric speech. Method: We propose a dual-component framework consisting of a universal module that generates language-independent speech representations and a language-specific intelligibility model that incorporates linguistic nuances. Additionally, we identify key barriers to cross-language intelligibility assessment, including data scarcity, annotation complexity, and limited linguistic insights, and present AI-driven solutions to overcome these challenges. Conclusion: Advances in AI offer transformative opportunities to enhance cross-language intelligibility assessment for dysarthric speech by balancing scalability across languages and adaptability by languages.

Problem

• Speech Impairment
• Multilingual Environment
• Communication Assistance

Innovation

• Bilingual AI Framework
• Cross-lingual Understanding
• Personalized Language Adaptation