Paper Info
Title
DeepDDK: A Deep Learning based Oral-Diadochokinesis Analysis Software
Abstract
Oromotor dysfunction caused by neurological disorders can result in significant speech and swallowing impairments. Current diagnostic methods to assess oromotor function are subjective and rely on perceptual judgments by clinicians. In particular, the widely used oral-diadochokinesis (oral-DDK) test, which requires rapid, alternate repetitions of speech-based syllables, is conducted and interpreted differently among clinicians. It is therefore prone to inaccuracy, which results in poor test reliability and poor clinical application. In this paper, we present a deep learning based software to extract quantitative data from the oral DDK signal, thereby transforming it into an objective diagnostic and treatment monitoring tool. The proposed software consists of two main modules: a fully automated syllable detection module and an interactive visualization and editing module that allows inspection and correction of automated syllable units. The DeepDDK software was evaluated on speech files corresponding to 9 different DDK syllables (e.g., “Pa”, “Ta”, “Ka”). The experimental results show robustness of both syllable detection and localization across different types of DDK speech tasks.
Year
DOI
Venue
2019
10.1109/BHI.2019.8834506
2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Keywords
Field
DocType
Diadochokinesis analysis,speech signal analysis,deep learning,event detection,event localization
Reliability (statistics),Analysis software,Computer science,Speech recognition,Robustness (computer science),Interactive visualization,Software,Artificial intelligence,Syllable,Deep learning,Perception
Conference
ISSN
ISBN
Citations 
2641-3590
978-1-7281-0849-0
0
PageRank 
References 
Authors
0.34
3
7
Name
Order
Citations
PageRank
Yang Yang Wang100.34
Ke Gao236924.62
Ashley M. Kloepper300.34
Yunxin Zhao4807121.74
Mili Kuruvilla-Dugdale501.01
Teresa E. Lever632.42
Filiz Bunyak758940.36