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Comparison of Automated Acoustic Methods for Oral Diadochokinesis Assessment in Amyotrophic Lateral Sclerosis

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Title: Comparison of Automated Acoustic Methods for Oral Diadochokinesis Assessment in Amyotrophic Lateral Sclerosis
Language: English
Authors: Novotny, Michal, Melechovsky, Jan, Rozenstoks, Kriss, Tykalova, Tereza, Kryze, Petr, Kanok, Martin, Klempir, Jiri, Rusz, Jan (ORCID 0000-0002-1036-3054)
Source: Journal of Speech, Language, and Hearing Research. Oct 2020 63(10):3453-3460.
Availability: American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
Peer Reviewed: Y
Page Count: 8
Publication Date: 2020
Document Type: Journal Articles
Reports - Research
Descriptors: Comparative Analysis, Diseases, Oral Language, Speech Communication, Syllables, Patients, Accuracy, Learning Processes, Bayesian Statistics, Language Processing, Diagnostic Tests, Audio Equipment, Psychomotor Skills, Acoustics
DOI: 10.1044/2020_JSLHR-20-00109
ISSN: 1092-4388
Abstract: Purpose: The purpose of this research note is to provide a performance comparison of available algorithms for the automated evaluation of oral diadochokinesis using speech samples from patients with amyotrophic lateral sclerosis (ALS). Method: Four different algorithms based on a wide range of signal processing approaches were tested on a sequential motion rate /pa/-/ta/-/ka/ syllable repetition paradigm collected from 18 patients with ALS and 18 age- and gender-matched healthy controls (HCs). Results: The best temporal detection of syllable position for a 10-ms tolerance value was achieved for ALS patients using a traditional signal processing approach based on a combination of filtering in the spectrogram, Bayesian detection, and polynomial thresholding with an accuracy rate of 74.4%, and for HCs using a deep learning approach with an accuracy rate of 87.6%. Compared to HCs, a slow diadochokinetic rate (p < 0.001) and diadochokinetic irregularity (p < 0.01) were detected in ALS patients. Conclusions: The approaches using deep learning or multiple-step combinations of advanced signal processing methods provided a more robust solution to the estimation of oral DDK variables than did simpler approaches based on the rough segmentation of the signal envelope. The automated acoustic assessment of oral diadochokinesis shows excellent potential for monitoring bulbar disease progression in individuals with ALS.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1281029
Database: ERIC
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  Data: American Speech-Language-Hearing Association. 2200 Research Blvd #250, Rockville, MD 20850. Tel: 301-296-5700; Fax: 301-296-8580; e-mail: slhr@asha.org; Web site: http://jslhr.pubs.asha.org
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  Data: 1092-4388
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  Label: Abstract
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  Data: Purpose: The purpose of this research note is to provide a performance comparison of available algorithms for the automated evaluation of oral diadochokinesis using speech samples from patients with amyotrophic lateral sclerosis (ALS). Method: Four different algorithms based on a wide range of signal processing approaches were tested on a sequential motion rate /pa/-/ta/-/ka/ syllable repetition paradigm collected from 18 patients with ALS and 18 age- and gender-matched healthy controls (HCs). Results: The best temporal detection of syllable position for a 10-ms tolerance value was achieved for ALS patients using a traditional signal processing approach based on a combination of filtering in the spectrogram, Bayesian detection, and polynomial thresholding with an accuracy rate of 74.4%, and for HCs using a deep learning approach with an accuracy rate of 87.6%. Compared to HCs, a slow diadochokinetic rate (p &lt; 0.001) and diadochokinetic irregularity (p &lt; 0.01) were detected in ALS patients. Conclusions: The approaches using deep learning or multiple-step combinations of advanced signal processing methods provided a more robust solution to the estimation of oral DDK variables than did simpler approaches based on the rough segmentation of the signal envelope. The automated acoustic assessment of oral diadochokinesis shows excellent potential for monitoring bulbar disease progression in individuals with ALS.
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        Value: 10.1044/2020_JSLHR-20-00109
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    PhysicalDescription:
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