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Models and Approaches for Comprehension of Dysarthric Speech Using Natural Language Processing: Systematic Review.

Benard Alaka¹, Bernard Shibwabo¹

¹School of Computing and Engineering Sciences, Strathmore University, Nairobi, Kenya.

JMIR Rehabilitation and Assistive Technologies

|October 27, 2023

Summary

This study found significant gaps in understanding dysarthric speech, particularly in incorporating listener-independent features. Future research should focus on semantic ontologies to improve meaning extraction for better dysarthric speech comprehension.

Keywords:

dysarthria familiarity meaning extraction ontology extraction speech comprehension speech contextualization topic knowledge

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Area of Science:

Speech and Language Processing
Biomedical Engineering
Artificial Intelligence in Healthcare

Background:

Dysarthric speech, characterized by motor speech production impairments, presents challenges in intelligibility and comprehension.
Recent attention has focused on improving the understanding of dysarthric speech.

Purpose of the Study:

To systematically review studies on dysarthric speech comprehension.
To examine meaning extraction techniques, applied methods, speech representations, and databases used in existing research.
To identify gaps in current approaches for generalizing speaker-listener underpinnings.

Main Methods:

A systematic literature review was conducted across 7 electronic databases.
30 studies were selected based on eligibility criteria related to natural language processing/understanding for dysarthric speech.
Methodological quality was assessed using the Cochrane risk-of-bias tool (RoB2) and TRIPOD.

Main Results:

A surge in proposed solutions for dysarthric speech comprehension was observed from 2020 onwards.
Most studies utilized speaker-dependent features, with prevalent use of vector representations for natural language understanding models.
Commonly used databases included TORGO and UA-Speech, with a preference for primary data for specific research.

Conclusions:

Significant gaps exist in dysarthric speech comprehension, notably the underutilization of listener and speech-independent features.
Current speech representations, meaning extraction modes, and data sources require enhancement.
Future research should develop models incorporating listener/speech-independent features via semantic ontologies for improved meaning extraction.