# Mitigating Scoring Errors and Compensating for Nonverbal Subtests in Speech-Based Dementia Assessment

arXiv: https://arxiv.org/abs/2606.18979 · HTML: https://mlsanity.com/paper/2606.18979/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 11 of 11)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [Detecting cognitive decline using speech only: The ADReSSo Challenge](https://mlsanity.com/paper/2104.09356/) (2021)
- [CrisperWhisper: Accurate Timestamps on Verbatim Speech Transcriptions](https://mlsanity.com/paper/2408.16589/) (2024)
- [Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge](https://mlsanity.com/paper/2301.05562/) (2023)
- [Large Language Models for Dysfluency Detection in Stuttered Speech](https://mlsanity.com/paper/2406.11025/) (2024)
- [Classifying Dementia in the Presence of Depression: A Cross-Corpus Study](https://mlsanity.com/paper/2308.08306/) (2023)
- [Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments using Acoustic Features](https://mlsanity.com/paper/2206.05018/) (2022)
- [Automated Evaluation of Standardized Dementia Screening Tests](https://mlsanity.com/paper/2206.06208/) (2022)
- [Pitfalls and Limits in Automatic Dementia Assessment](https://mlsanity.com/paper/2508.04512/) (2025)
- [The PARLO Dementia Corpus: A German Multi-Center Resource for Alzheimer's Disease](https://mlsanity.com/paper/2603.03471/) (2026)

## Abstract (arXiv)

Early detection of cognitive impairment relies on neuropsychological tests to minimize subjectivity by assessing multiple cognitive domains. Speech-based evaluation can support diagnostics and improve accessibility, but transcription errors and the omission of nonverbal subtests (e.g., motor skills) limit accuracy. Beyond conventional test scores, speech-derived features can provide additional insights into cognitive status. This study investigates the speech-based evaluation of the German "Syndrom-Kurz-Test," a standardized dementia screening test comprising verbal and motor subtests. We train models that integrate transcript-derived scores and Whisper embeddings per verbal subtest to reduce scoring errors. To compensate for missing motor subtests, we then leverage these fused representations to approximate expert overall ratings. Despite omitting subtests, our models strongly correlate with expert ratings and efficiently and accurately discriminate between cognitive status groups.