Cross-Lingual Transfer Learning for Alzheimer's Detection From Spontaneous Speech

TL;DR

This paper demonstrates that a speech-based Alzheimer's detection model trained on English can be effectively transferred to Greek with minimal data, achieving high accuracy and competitive ranking in a challenge.

Contribution

It introduces a cross-lingual transfer learning approach for Alzheimer's detection from spontaneous speech, showing effective generalization across languages with limited Greek data.

Findings

Achieved 82.6% accuracy in AD detection on Greek speech

Ranked 2nd in the ICASSP-2023 challenge

Successfully transferred models with only 8 Greek samples

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disease most often associated with memory deficits and cognitive decline. With the aging population, there has been much interest in automated methods for cognitive impairment detection. One approach that has attracted attention in recent years is AD detection through spontaneous speech. While the results are promising, it is not certain whether the learned speech features can be generalized across languages. To fill this gap, the ADReSS-M challenge was organized. This paper presents our submission to this ICASSP-2023 Signal Processing Grand Challenge (SPGC). The model was trained on 228 English samples of a picture description task and was transferred to Greek using only 8 samples. We obtained an accuracy of 82.6% for AD detection, a root-mean-square error of 4.345 for cognitive score prediction, and ranked 2nd place in the competition out of 24 competitors.