Training classification models on clinical speech is a time-saving and effective solution for many healthcare challenges, such as screening for Alzheimer’s Disease over the phone. One of the primary limiting factors of the success of artificial intelligence (AI) solutions is the amount of relevant data available. Clinical data is expensive to collect, not sufficient for large-scale machine learning or neural methods, and often not shareable between institutions due to data protection laws. With the increasing demand for AI in health systems, generating synthetic clinical data that maintains the nuance of underlying patient pathology is the next pressing task. Previous work has shown that automated evaluation of clinical speech tasks via automatic speech recognition (ASR) is comparable to manually annotated results in diagnostic scenarios even though ASR systems produce errors during the transcription process. In this work, we propose to generate synthetic clinical data by simulating ASR deletion errors on the transcript to produce additional data. We compare the synthetic data to the real data with traditional machine learning methods to test the feasibility of the proposed method. Using a dataset of 50 cognitively impaired and 50 control Dutch speakers, ten additional data points are synthetically generated for each subject, increasing the training size for 100 to 1000 training points. We find consistent and comparable performance of models trained on only synthetic data (AUC=0.77) to real data (AUC=0.77) in a variety of traditional machine learning scenarios. Additionally, linear models are not able to distinguish between real and synthetic data.

Effective management of dementia hinges on timely detection and precise diagnosis of the underlying cause of the syndrome at an early mild cognitive impairment (MCI) stage. Verbal fluency tasks are among the most often applied tests for early dementia detection due to their efficiency and ease of use. In these tasks, participants are asked to produce as many words as possible belonging to either a semantic category (SVF task) or a phonemic category (PVF task). Even though both SVF and PVF share neurocognitive function profiles, the PVF is typically believed to be less sensitive to measure MCI-related cognitive impairment and recent research on fine-grained automatic evaluation of VF tasks has mainly focused on the SVF. Contrary to this belief, we show that by applying state-of-the-art semantic and phonemic distance metrics in automatic analysis of PVF word productions, in-depth conclusions about production strategy of MCI patients are possible. Our results reveal a dissociation between semantically- and phonemically-guided search processes in the PVF. Specifically, we show that subjects with MCI rely less on semantic- and more on phonemic processes to guide their word production as compared to healthy controls (HC). We further show that semantic similarity-based features improve automatic MCI versus HC classification by 29% over previous approaches for the PVF. As such, these results point towards the yet underexplored utility of the PVF for in-depth assessment of cognition in MCI.