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Review
. 2024 Dec:167:105922.
doi: 10.1016/j.neubiorev.2024.105922. Epub 2024 Oct 18.

From prodromal stages to clinical trials: The promise of digital speech biomarkers in Parkinson's disease

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Free article
Review

From prodromal stages to clinical trials: The promise of digital speech biomarkers in Parkinson's disease

Jan Rusz et al. Neurosci Biobehav Rev. 2024 Dec.
Free article

Abstract

Speech impairment is a common and disabling symptom in Parkinson's disease (PD), affecting communication and quality of life. Advances in digital speech processing and artificial intelligence have revolutionized objective speech analysis. Given the complex nature of speech impairment, acoustic speech analysis offers unique biomarkers for neuroprotective treatments from the prodromal stages of PD. Digital speech biomarkers can monitor levodopa-induced motor complications, detect the effects of deep brain stimulation, and provide feedback for behavioral speech therapy. This review updates the mechanisms underlying speech impairment, the impact of speech phenotypes, and the effects of interventions on speech. We evaluate the strengths, potential weaknesses, and suitability of promising digital speech biomarkers in PD for capturing disease progression and treatment efficacy. Additionally, we explore the translational potential of PD speech biomarkers to other neuropsychiatric diseases, offering insights into motion, cognition, and emotion. Finally, we highlight knowledge gaps and suggest directions for future research to enhance the use of quantitative speech measures in disease-modifying clinical trials. The findings demonstrate that one year is sufficient to detect disease progression in early PD through speech biomarkers. Voice quality, pitch, loudness, and articulation measures appear to capture the efficacy of treatment interventions most effectively. Certain speech features, such as loudness and articulation rate, behave oppositely in different neurological diseases, offering valuable insights for differential diagnosis. In conclusion, this review highlights speech as a biomarker in tracking disease progression, especially in the prodromal stages of PD, and calls for further longitudinal studies to establish its efficacy across diverse populations.

Keywords: Acoustic; Dysarthria; Machine learning; Neurological diseases; Parkinson’s disease; Speech; Voice.

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Conflict of interest statement

Declaration of Competing Interest All authors declare that they have no conflicts of interest.