Ten Years of Research on Automatic Voice and Speech Analysis of People With Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review Article

Abstract

Background: The field of voice and speech analysis has become increasingly popular over the last 10 years, and articles on its use in detecting neurodegenerative diseases have proliferated. Many studies have identified characteristic speech features that can be used to draw an accurate distinction between healthy aging among older people and those with mild cognitive impairment and Alzheimer's disease. Speech analysis has been singled out as a cost-effective and reliable method for detecting the presence of both conditions. In this research, a systematic review was conducted to determine these features and their diagnostic accuracy.Methods: Peer-reviewed literature was located across multiple databases, involving studies that apply new procedures of automatic speech analysis to collect behavioral evidence of linguistic impairments along with their diagnostic accuracy on Alzheimer's disease and mild cognitive impairment. The risk of bias was assessed by using JBI and QUADAS-2 checklists.Results: Thirty-five papers met the inclusion criteria; of these, 11 were descriptive studies that either identified voice features or explored their cognitive correlates, and the rest were diagnostic studies. Overall, the studies were of good quality and presented solid evidence of the usefulness of this technique. The distinctive acoustic and rhythmic features found are gathered. Most studies record a diagnostic accuracy over 88% for Alzheimer's and 80% for mild cognitive impairment.Conclusion: Automatic speech analysis is a promising tool for diagnosing mild cognitive impairment and Alzheimer's disease. The reported features seem to be indicators of the cognitive changes in older people. The specific features and the cognitive changes involved could be the subject of further research.

Using Digital Speech Assessments to Detect Early Signs of Cognitive Impairment

“…Advances in signal processing and natural language processing have enabled objective analysis of speech samples for both their acoustic properties and linguistic content, providing a window into motor, linguistic and cognitive abilities. Most importantly, previous research has extensively shown that speech patterns are affected in MCI and AD, demonstrating the clinical relevance of speech for detecting cognitive impairment and dementia (8)(9)(10)(11)(12).…”

“…Much of the previous work studying speech patterns in AD using automated speech analysis has focused on multivariate classification models to differentiate those with AD or MCI from healthy controls based on their speech (9)(10)(11). These models generally achieve high accuracy (80-90%) demonstrating that there are robust differences between clinical groups based on speech characteristics (13)(14)(15)(16)(17)(18)(19)(20).…”

Evaluation of a speech-based AI system for early detection of Alzheimer’s disease remotely via smartphones

“…Research documents changes in vocal and linguistic speech patterns in AD, primarily in cohorts with more progressed AD and without biomarker confirmation 13,25 . The current findings show detectable changes in speech occurring earlier in the disease process.…”

Validation of a remote and fully automated story recall task to assess for early cognitive impairment in older adults: a longitudinal case-control observational study

“…Speech responses are a common component of cognitive tests, however data generated in these tests, including those reported in this study, often relate to simple pass/fail characteristics of response accuracy. New metrics using audio-and text-based AI models to target other changes measurable in speech data (acoustic [30,31], semantic [32][33][34][35], linguistic [31]) in early-stage Alzheimer's disease could further leverage the information content of ASRTs, developing a new class of powerful, fully automated speech biomarkers.…”