# Spoken Language Biomarkers for Detecting Cognitive Impairment

arXiv: https://arxiv.org/abs/1710.07551 · HTML: https://mlsanity.com/paper/1710.07551/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2017.

## Built on by (most cited first, 3 of 3)

- [Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations](https://mlsanity.com/paper/2305.08099/) (2023)
- [On the Within-class Variation Issue in Alzheimer's Disease Detection](https://mlsanity.com/paper/2409.16322/) (2024)
- [Recognizing Dementia from Neuropsychological Tests with State Space Models](https://mlsanity.com/paper/2507.10311/) (2025)

## Abstract (arXiv)

In this study we developed an automated system that evaluates speech and language features from audio recordings of neuropsychological examinations of 92 subjects in the Framingham Heart Study. A total of 265 features were used in an elastic-net regularized binomial logistic regression model to classify the presence of cognitive impairment, and to select the most predictive features. We compared performance with a demographic model from 6,258 subjects in the greater study cohort (0.79 AUC), and found that a system that incorporated both audio and text features performed the best (0.92 AUC), with a True Positive Rate of 29% (at 0% False Positive Rate) and a good model fit (Hosmer-Lemeshow test > 0.05). We also found that decreasing pitch and jitter, shorter segments of speech, and responses phrased as questions were positively associated with cognitive impairment.