Monitoring ALS from speech articulation kinematics

AbstractPatients affected by amyotrophic lateral sclerosis (ALS) show specific dysarthria in their speech resulting in specific marks which could be used to detect early symptoms and monitor the evolution of the disease in time. Classically articulation marks have been mainly based on static premises. Articulation kinematics from acoustic correlates may help in producing measurements depending on the dynamic behaviour of speech. Specifically, distribution functions from the absolute kinematic velocity estimated on a simplified articulation model can be used in establishing distances based on information theory concepts between running speech segments from patients and controls. As an example, several cases of ALS were studied longitudinally using this methodology. The study shows that the performance of dynamic articulation quality correlates may be sensitive and robust in tracking illness progress. Conclusions foresee the use of speech as a valuable monitoring methodology for ALS timely neurodegenerative progression.

[1]  Yohan Payan,et al.  A control model of human tongue movements in speech , 1997, Biological Cybernetics.

[2]  John H. L. Hansen,et al.  Discrete-Time Processing of Speech Signals , 1993 .

[3]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[4]  T. Bocci,et al.  How to Assess Disease’s Severity and Monitor Patients with Amyotrophic Lateral Sclerosis: Lessons from Neurophysiology , 2012 .

[5]  Kristofer E. Bouchard,et al.  High-Resolution, Non-Invasive Imaging of Upper Vocal Tract Articulators Compatible with Human Brain Recordings , 2016, PloS one.

[6]  Yana Yunusova,et al.  Speech in ALS: Longitudinal Changes in Lips and Jaw Movements and Vowel Acoustics. , 2013, Journal of medical speech-language pathology.

[7]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[8]  E. Mohammadi,et al.  Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence , 2017, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[9]  M. Lindstrom,et al.  Articulatory movements during vowels in speakers with dysarthria and healthy controls. , 2008, Journal of speech, language, and hearing research : JSLHR.

[10]  O. Hermine,et al.  ALS Clinical Trials Review: 20 Years of Failure. Are We Any Closer to Registering a New Treatment? , 2017, Front. Aging Neurosci..

[11]  Jennifer L. Spielman,et al.  Formant centralization ratio: a proposal for a new acoustic measure of dysarthric speech. , 2010, Journal of speech, language, and hearing research : JSLHR.

[12]  María Victoria Rodellar Biarge,et al.  Monitoring amyotrophic lateral sclerosis by biomechanical modeling of speech production , 2015, Neurocomputing.

[13]  J. Cedarbaum,et al.  The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function , 1999, Journal of the Neurological Sciences.

[14]  Marcos Faúndez-Zanuy,et al.  On Automatic Diagnosis of Alzheimer’s Disease Based on Spontaneous Speech Analysis and Emotional Temperature , 2013, Cognitive Computation.

[15]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.

[16]  Jorge Proença,et al.  Computational Processing of the Portuguese Language , 2014, Lecture Notes in Computer Science.

[17]  Gary L. Pattee,et al.  Bulbar and speech motor assessment in ALS: Challenges and future directions , 2013, Amyotrophic lateral sclerosis & frontotemporal degeneration.

[18]  Christopher Dromey,et al.  Assessing correlations between lingual movements and formants , 2013, Speech Commun..

[19]  S. Cavallaro,et al.  Selection and Prioritization of Candidate Drug Targets for Amyotrophic Lateral Sclerosis Through a Meta-Analysis Approach , 2017, Journal of Molecular Neuroscience.

[20]  Krestina L. Amon,et al.  Interdisciplinary eHealth for the care of people living with traumatic brain injury: A systematic review , 2017, Brain injury.

[21]  A G Hannam,et al.  A dynamic model of jaw and hyoid biomechanics during chewing. , 2008, Journal of biomechanics.