An ensemble of six speech and text models, mixing pause features, LLM macrodescriptors, and neural embeddings, achieves 59.3% macro F1 on the PROCESS three-class cognitive decline test set.
Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge
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abstract
This work describes our group's submission to the PROCESS Challenge 2024, with the goal of assessing cognitive decline through spontaneous speech, using three guided clinical tasks. This joint effort followed a holistic approach, encompassing both knowledge-based acoustic and text-based feature sets, as well as LLM-based macrolinguistic descriptors, pause-based acoustic biomarkers, and multiple neural representations (e.g., LongFormer, ECAPA-TDNN, and Trillson embeddings). Combining these feature sets with different classifiers resulted in a large pool of models, from which we selected those that provided the best balance between train, development, and individual class performance. Our results show that our best performing systems correspond to combinations of models that are complementary to each other, relying on acoustic and textual information from all three clinical tasks.
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Tackling Cognitive Impairment Detection from Speech: A submission to the PROCESS Challenge
An ensemble of six speech and text models, mixing pause features, LLM macrodescriptors, and neural embeddings, achieves 59.3% macro F1 on the PROCESS three-class cognitive decline test set.