OBJECTIVE: Voice tremor represents a common but frequently overlooked clinical feature of neurological disease. Therefore, we aimed to quantitatively and objectively assess the characteristics of voice tremor in a large sample of patients with various progressive neurological diseases. METHODS: Voice samples were acquired from 240 patients with neurological disease and 40 healthy controls. The robust automated method was designed, allowing precise tracking of multiple tremor frequencies and distinguish pathological from the physiological tremor. RESULTS: Abnormal tremor was revealed in Huntington's disease (65%), essential tremor (50%), multiple system atrophy (40%), cerebellar ataxia (40%), amyotrophic lateral sclerosis (40%), progressive supranuclear palsy (25%), Parkinson's disease (20%), cervical dystonia (10%), and multiple sclerosis (8%) but not in controls. Low-frequency voice tremor (<4 Hz) was common in all investigated diseases, whereas medium tremor frequencies (4-7 Hz) were specific for movement disorders of Parkinson's disease, multiple system atrophy, essential tremor, and cervical dystonia. CONCLUSIONS: Careful estimation of vocal tremor may help with accurate diagnosis and tailored treatment. SIGNIFICANCE: This study provides (i) more insights into the pathophysiology of vocal tremor in a wide range of neurological diseases and (ii) an accurate method for estimation of vocal tremor suitable for clinical practice.

• MeSH
• Speech Acoustics * MeSH
• Adult MeSH
• Electromyography methods   MeSH
• Essential Tremor diagnosis   physiopathology   MeSH
• Fourier Analysis * MeSH
• Voice Quality physiology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Nervous System Diseases diagnosis   physiopathology   MeSH
• Voice Disorders diagnosis   physiopathology   MeSH
• Disease Progression * MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

For generations, the evaluation of speech abnormalities in neurodegenerative disorders such as Parkinson's disease (PD) has been limited to perceptual tests or user-controlled laboratory analysis based upon rather small samples of human vocalizations. Our study introduces a fully automated method that yields significant features related to respiratory deficits, dysphonia, imprecise articulation and dysrhythmia from acoustic microphone data of natural connected speech for predicting early and distinctive patterns of neurodegeneration. We compared speech recordings of 50 subjects with rapid eye movement sleep behaviour disorder (RBD), 30 newly diagnosed, untreated PD patients and 50 healthy controls, and showed that subliminal parkinsonian speech deficits can be reliably captured even in RBD patients, which are at high risk of developing PD or other synucleinopathies. Thus, automated vocal analysis should soon be able to contribute to screening and diagnostic procedures for prodromal parkinsonian neurodegeneration in natural environments.

• MeSH
• Biomarkers MeSH
• Adult MeSH
• Respiration MeSH
• Dysphonia physiopathology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Parkinson Disease diagnosis   physiopathology   MeSH
• REM Sleep Behavior Disorder physiopathology   MeSH
• Articulation Disorders physiopathology   MeSH
• Pattern Recognition, Automated methods   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Speech Acoustics * MeSH
• Adult MeSH
• Huntington Disease * diagnosis   complications   MeSH
• Clinical Studies as Topic MeSH
• Middle Aged MeSH
• Humans MeSH
• Speech Production Measurement * methods   instrumentation   MeSH
• Speech Disorders * diagnosis   MeSH
• Speech Sound Disorder * diagnosis   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Research Support, Non-U.S. Gov't MeSH