Acoustic Change Over Time in Spastic and/or Flaccid Dysarthria in Motor Neuron Diseases

Abstract

Purpose: This study aims to investigate acoustic change over time as biomarkers to differentiate among spastic–flaccid dysarthria associated with amyotrophic lateral sclerosis (ALS), spastic dysarthria associated with primary lateral sclerosis (PLS), flaccid dysarthria associated with spinal and bulbar muscular atrophy (SBMA), and to explore how these acoustic parameters are affected by dysarthria severity. Method: Thirty-three ALS patients with mixed flaccid–spastic dysarthria, 17 PLS patients with pure spastic dysarthria, 18 SBMA patients with pure flaccid dysarthria, and 70 controls, all French speakers, were included in the study. Speakers produced vowel–glide sequences targeting different vocal tract shape changes. The mean and coefficient of variation of the total squared change of mel frequency cepstral coefficients were used to capture the degree and variability of acoustic changes linked to vocal tract modifications over time. Differences in duration of acoustic events were also measured. Results: All pathological groups showed significantly less acoustic change compared to controls, reflecting less acoustic contrast in sequences. Spastic and mixed spastic–flaccid dysarthric speakers showed smaller acoustic changes and slower sequence production compared to flaccid dysarthria. For dysarthria subtypes associated with a spastic component, reduced degree of acoustic change was also associated with dysarthria severity. Conclusions: The acoustic parameters partially differentiated among the dysarthria subtypes in relation to motor neuron diseases. While similar acoustic patterns were found in spastic–flaccid and spastic dysarthria, crucial differences were found between these two subtypes relating to variability. The acoustic patterns were much more variable in ALS. This method forms a promising clinical tool as a diagnostic marker of articulatory impairment, even at mild stage of dysarthria progression in all subtypes.

Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s

“…These articulatory impairments could reflect reduced muscular force at the level of the tongue [ 46 ] or the jaw, as for patients with ALS. However, in the Kennedy group compared to the ALS group, articulatory distortions are not linked to a reduced speech rate, as also found in [ 43 ].…”

“…Flaccid dysarthria associated with Kennedy disease can be misdiagnosed, at least at onset of the disease, as ALS (associated with mixed dysarthria with flaccid and spastic components). However, the differential diagnosis between the two dysarthria has strong implications for patient management [ 42 , 43 ]. Interestingly, our Kennedy/ALS classification allows us to predict group membership with 100% accuracy, with a strong contribution of the deviance in the DDK rate and voice, which are present only in the ALS group (probably linked to its spastic component).…”

A clinical bulbar assessment scale (CBAS) for amyotrophic lateral sclerosis

“…During speech production, respiratory capacity is based on control and modification of the air stream (eg, loudness, pitch variation) 57 . Vowel prolongation tasks aid perceptual identification of vocal UMN (eg, harsh, strain) and LMN (eg, breathy, hoarse) features 23,27,58,59 . A standard reading task routinely used in speech analysis was included to establish speaking rate and participant ratings of associated effort and fatigue 23,60 …”

“…57 Vowel prolongation tasks aid perceptual identification of vocal UMN (eg, harsh, strain) and LMN (eg, breathy, hoarse) features. 23,27,58,59 A standard reading task routinely used in speech analysis was included to establish speaking rate and participant ratings of associated effort and fatigue. 23,60 The Speech Intelligibility Test (SIT) is a standardized assessment of speech intelligibility for English-speaking adults with dysarthria.…”

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

“…[3][4][5][6][7] Many groups have also found grounds for the analysis of voice for the detection and progression of neurological disorders, such as Parkinson's, Alzheimer's, Huntington's, and dementia. [8][9][10][11][12][13][14][15][16] Additional areas of investigation include pediatric and adult voice disorders, and mental health disorders. [17][18][19][20][21][22] As in other fields of machine learning, the quality of voice AI models is highly dependent on the quality of the voice datasets used for model training.…”