Towards Automatic Detection of Amyotrophic Lateral Sclerosis from Speech Acoustic and Articulatory Samples

Wang, Jun; Kothalkar, Prasanna V.; Cao, Beiming; Heitzman, Daragh

doi:10.21437/interspeech.2016-1542

Cited by 45 publications

(55 citation statements)

References 19 publications

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“…The feasibility of using speech signals revealed promising results in a number of recent studies for disease detection and severity estimation in depression [13, 14], traumatic brain injury [15], and Parkinson’s disease detection or severity estimation [16, 17, 18, 19, 20, 21]. Our recent work also showed the feasibility of detection of ALS from speech samples [22]. Estimating the progression of ALS from speech samples using data-driven approaches, however, has rarely been attempted.…”

Section: Introductionmentioning

confidence: 86%

“…The script provided in [22] was used for extracting acoustic and articulatory features from acoustic and articulatory motion data, respectively. The script was modified based on that provided in [34].…”

Section: Methodsmentioning

confidence: 99%

“…However, low frequency articulatory data do not contain these information. The following feature groups were disabled for articulatory feature extraction [22]: Jitter, Shimmer, logHNR, Rfilt, Rasta, MFCC, Harmonicity, and Spectral Rolloff.…”

Section: Methodsmentioning

confidence: 99%

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Predicting Intelligible Speaking Rate in Individuals with Amyotrophic Lateral Sclerosis from a Small Number of Speech Acoustic and Articulatory Samples

Wang

Kothalkar

Kim

et al. 2016

7th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT 2016)

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Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurological disease that affects the speech motor functions, resulting in dysarthria, a motor speech disorder. Speech and articulation deterioration is an indicator of the disease progression of ALS; timely monitoring of the disease progression is critical for clinical management of these patients. This paper investigated machine prediction of intelligible speaking rate of nine individuals with ALS based on a small number of speech acoustic and articulatory samples. Two feature selection techniques - decision tree and gradient boosting - were used with support vector regression for predicting the intelligible speaking rate. Experimental results demonstrated the feasibility of predicting intelligible speaking rate from only a small number of speech samples. Furthermore, adding articulatory features to acoustic features improved prediction performance, when decision tree was used as the feature selection technique.

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Section: Introductionmentioning

confidence: 86%

Section: Methodsmentioning

confidence: 99%

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Predicting Intelligible Speaking Rate in Individuals with Amyotrophic Lateral Sclerosis from a Small Number of Speech Acoustic and Articulatory Samples

Wang

Kothalkar

Kim

et al. 2016

7th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT 2016)

Self Cite

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“…The features were selected from the OpenSMILE [18] set, and the same features were applied to both males and females. A second study explored classification of the ALS condition using the same features applied to SVM and DNN classifier [19]. Alternate approaches used formant trajectories to classify the ALS condition [20], correlated formants with articulatory patterns [16], or used fractal features [21].…”

Section: Introductionmentioning

confidence: 99%

Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis

Norel¹,

Pietrowicz²,

Agurto³

et al. 2018

Preprint

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ALS is a fatal neurodegenerative disease with no cure. Experts typically measure disease progression via the ALSFRS-R score, which includes measurements of various abilities known to decline. We propose instead the use of speech analysis as a proxy for ALS progression. This technique enables 1) frequent non-invasive, inexpensive, longitudinal analysis, 2) analysis of data recorded in the wild, and 3) creation of an extensive ALS databank for future analysis. Patients and trained medical professionals need not be co-located, enabling more frequent monitoring of more patients from the convenience of their own homes. The goals of this study are the identification of acoustic speech features in naturalistic contexts which characterize disease progression and development of machine models which can recognize the presence and severity of the disease. We evaluated subjects from the Prize4Life Israel dataset, using a variety of frequency, spectral, and voice quality features. The dataset was generated using the ALS Mobile Analyzer, a cellphone app that collects data regarding disease progress using a self-reported ALSFRS-R questionnaire and several active tasks that measure speech and motor skills. Classification via leavefive-subjects-out cross-validation resulted in an accuracy rate of 79% (61% chance) for males and 83% (52% chance) for females.

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“…Early diagnosis is crucial for a better prognosis in the treatment of the disease and, to date, no other method offers such low cost and simplicity as voice-assisted diagnosis. Hence, evaluation of speech and speaking performance may be well suited for ALS early detection and monitoring [15][16][17][18][19][20][21]. In fact, in a large cohort, more than 33% of patients reported to have dysarthria problems [22].…”

Section: Diagnosis and Impact On Phonation Processmentioning

confidence: 99%

Voice-Based Classification of Amyotrophic Lateral Sclerosis: Where Are We and Where Are We Going? A Systematic Review

et al. 2019

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Background: Amyotrophic lateral sclerosis (ALS) is a fatal progressive motor neuron disease. People with ALS demonstrate various speech problems. Summary: We aim to provide an overview of studies concerning the diagnosis of ALS based on the analysis of voice samples. The main focus is on the feasibility of the use of voice and speech assessment as an effective method to diagnose the disease, either in clinical or pre-clinical conditions, and to monitor the disease progression. Specifically, we aim to examine current knowledge on: (a) voice parameters and the data models that can, most effectively, provide robust results; (b) the feasibility of a semi-automatic or automatic diagnosis and outcomes; and (c) the factors that can improve or restrict the use of such systems in a real-world context. Key Messages: The studies already carried out on the possibility of diagnosis of ALS using the voice signal are still sparse but all point to the importance, feasibility and simplicity of this approach. Most co-horts are small which limits the statistical relevance and makes it difficult to infer broader conclusions. The set of features used, although diverse, is quite circumscribed. ALS is difficult to diagnose early because it may mimic several other neurological diseases. Promising results were found for the automatic detection of ALS from speech samples and this can be a feasible process even in pre-symptomatic stages. Improved guidelines must be set in order to establish a robust decision model.

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Towards Automatic Detection of Amyotrophic Lateral Sclerosis from Speech Acoustic and Articulatory Samples

Cited by 45 publications

References 19 publications

Predicting Intelligible Speaking Rate in Individuals with Amyotrophic Lateral Sclerosis from a Small Number of Speech Acoustic and Articulatory Samples

Predicting Intelligible Speaking Rate in Individuals with Amyotrophic Lateral Sclerosis from a Small Number of Speech Acoustic and Articulatory Samples

Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis

Voice-Based Classification of Amyotrophic Lateral Sclerosis: Where Are We and Where Are We Going? A Systematic Review

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