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

Autor/inn/en	Novotny, Michal; Melechovsky, Jan; Rozenstoks, Kriss; Tykalova, Tereza; Kryze, Petr; Kanok, Martin; Klempir, Jiri; Rusz, Jan
Titel	Comparison of Automated Acoustic Methods for Oral Diadochokinesis Assessment in Amyotrophic Lateral Sclerosis
Quelle	In: Journal of Speech, Language, and Hearing Research, 63 (2020) 10, S.3453-3460 (8 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Rusz, Jan)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1092-4388
Schlagwörter	Comparative Analysis; Diseases; Oral Language; Speech Communication; Syllables; Patients; Accuracy; Learning Processes; Bayesian Statistics; Language Processing; Diagnostic Tests; Audio Equipment; Psychomotor Skills; Acoustics + Suchen Sie Ihr Suchwort? Disease; Krankheit; Oral interpretation; Mündlicher Sprachgebrauch; Silbe; Patient; Learning process; Lernprozess; Sprachverarbeitung; Diagnostic test; Diagnostischer Test; Audio-CD; Psychomotorische Aktivität; Akustik
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. (As Provided).
Anmerkungen	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
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01