Diagnosing dementia early is crucial in mitigating the consequences of the disease for patients, their care-givers and relatives. We present automatic screening for personal transition into dementia from speech using information from more than one point in time. Using conversational speech data from the ILSE corpus we screen subjects if they transition from a cognitively healthy state to a state of dementia. We use both acoustic and linguistic features from two pipelines of feature extraction: the manual pipeline uses manual transcriptions while the fully automatic pipeline uses transcriptions created by automatic speech recognition (ASR). Using these two different feature extraction pipelines we automatically screen for dementia transition, where the fully automatic pipeline performs the whole screening process fully automatically. Our results show the features extracted from automatic transcriptions outperform the features extracted from the manual transcriptions.
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