Feature Selection and Text Embedding for Detecting Dementia from Spontaneous Cantonese

Xiaoquan Ke, Man Wai Mak, Helen Meng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Dementia is a severe cognitive impairment that affects the health of older adults and creates a burden on their families and caretakers. This paper analyzes diverse hand-crafted features extracted from spoken languages and selects the most discriminative ones for dementia detection. Recently, the performance of dementia detection has been significantly improved by utilizing Transformer-based models that automatically capture the structural and linguistic properties of spoken languages. We investigate Transformer-based features and propose an end-to-end system for dementia detection. We also explore recent ASR and representation learning frameworks, such as Wav2vec 2.0 and Hubert, for transcribing a Cantonese corpus that contains recordings of older adults describing the rabbit story. We investigate using disfluency patterns (DP) in spontaneous speech to enhance the recognized word sequences for the Transformer-based feature extractor. Results show that fine-tuning the feature extractor using the enhanced word sequences can improve dementia detection performance.
Original languageEnglish
Title of host publicationICASSP
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
Publication statusPublished - Jun 2023


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