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Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models
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In this paper, we combined linguistic complexity and (dis)fluency features with pretrained language models for the task of Alzheimer's disease detection of the 2021 ADReSSo (Alzheimer's Dementia Recognition through Spontaneous Speech) challenge. An accuracy of 83.1% was achieved on the test set, which amounts to an improvement of 4.23% over the baseline model. Our best-performing model that integrated component models using a stacking ensemble technique performed equally well on cross-validation and test data, indicating that it is robust against overfitting.
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Cited by 2 Pith papers
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Leveraging Cascaded Binary Classification and Multimodal Fusion for Dementia Detection through Spontaneous Speech
A cascaded two-stage classifier and a multimodal feature ensemble beat the PROCESS 2025 challenge baselines for dementia detection and MMSE score prediction from spontaneous speech.
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Leveraging Prompt Learning and Pause Encoding for Alzheimer's Disease Detection
Prompt-based fine-tuning with pause encoding reaches a maximum 95.8% accuracy for Alzheimer's detection on ADReSS transcripts, while the mean over random seeds is 87.9%.
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