Mitigating Scoring Errors and Compensating for Nonverbal Subtests in Speech-Based Dementia Assessment
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Electrical Engineering and Systems Science > Audio and Speech Processing
Title:Mitigating Scoring Errors and Compensating for Nonverbal Subtests in Speech-Based Dementia Assessment
Abstract: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.
| Comments: | Accepted at INTERSPEECH 2026 |
| Subjects: | Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2606.18979 [eess.AS] |
| (or arXiv:2606.18979v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18979
arXiv-issued DOI via DataCite (pending registration)
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