DeepDDK: A Deep Learning based Oral-Diadochokinesis Analysis Software.

Area of Science:

• Neurology
• Speech-Language Pathology
• Biomedical Engineering

Background:

• Neurological disorders frequently cause oromotor dysfunction, leading to speech and swallowing difficulties.
• Current methods for assessing oromotor function, like the oral-diadochokinesis (oral-DDK) test, are subjective and lack reliability.
• Variability in oral-DDK test administration and interpretation hinders accurate diagnosis and treatment monitoring.

Purpose of the Study:

• To develop and evaluate a deep learning-based software, DeepDDK, for objective quantitative analysis of oral-DDK speech signals.
• To transform the subjective oral-DDK test into a reliable diagnostic and treatment monitoring tool for oromotor dysfunction.

Main Methods:

• The study presents DeepDDK software featuring automated syllable detection and interactive editing modules.
• The software was trained and validated using speech files from 9 distinct oral-DDK syllables (e.g., "Pa", "Ta", "Ka").
• Evaluation focused on the robustness of syllable detection and localization across various DDK speech tasks.

Main Results:

• The DeepDDK software demonstrated robust performance in detecting and localizing syllables within oral-DDK speech tasks.
• Quantitative data extraction from oral-DDK signals was achieved, moving towards objective assessment.
• The system's accuracy was validated across different DDK syllable types.

Conclusions:

• DeepDDK offers a promising objective approach to assessing oromotor function, improving upon subjective clinical methods.
• The software has the potential to enhance diagnostic accuracy and facilitate effective treatment monitoring for patients with neurological disorders.
• This deep learning tool represents a significant advancement in the clinical application of oral-DDK testing.