Screening speech disorders in progressive neurological diseases via long-term average spectrum.

Abstract

The long-term averaged spectrum (LTAS) may provide a universal method for capturing distinct patterns of dysarthria. This study aimed to evaluate the sensitivity of LTAS descriptors in a broad range of neurological diseases and various types and severities of dysarthria. Four spectral moments of spectral mean, spectral standard deviation, spectral skewness and spectral kurtosis based on LTAS were computed for reading passage collected from 461 speakers, including 306 healthy controls and 155 neurological patients secondary to Parkinson's disease (PD), progressive supranuclear palsy, multiple system atrophy (MSA), Huntington's disease, essential tremor, cerebellar ataxia (CA), multiple sclerosis (MS), and amyotrophic lateral sclerosis. Compared to controls, the spectral mean was significantly lower in PD and MS while elevated in CA. Significantly changed LTAS features were observed only in hypokinetic dysarthria and in mixed dysarthrias manifesting hypokinetic elements. Although LTAS features differed between controls and patients with varying degrees of dysarthria, there was no progressive increase in dysarthria severity. Our findings suggest that LTAS-based speech analysis may provide valuable cues to aid differential diagnosis among neurological diseases with overlapping clinical features. LTAS appears more informative when applied to specific diseases than to pooled dysarthria types arising from diverse neurological etiologies.