CONSEN: Complementary and Simultaneous Ensemble for Alzheimer’s Disease Detection and MMSE Score Prediction
Longbin Jin, Yealim Oh, Hyunseo Kim, Hyuntaek Jung, Hyo Jin Jon, Jung Eun Shin, Eun Yi Kim
Abstract
This paper proposes a novel method for Alzheimer’s disease detection and MMSE prediction using a complementary and simultaneous ensemble (CONSEN) algorithm based on multilingual spontaneous speech. We define pause and intervention of speech to form disfluency features, as well as several acoustic features to train generalized models. With the help of the proposed CONSEN algorithm, our model achieves the best performance of 86.69% for AD detection and 3.727 RMSE for MMSE prediction, which is placed first rank in both tasks in ICASSP Signal Processing Grand Challenge: ADReSS-M Challenge 2023.
Topics & Concepts
Computer scienceRank (graph theory)Minimum mean square errorArtificial intelligenceSpeech recognitionPattern recognition (psychology)MathematicsStatisticsEstimatorCombinatoricsSpeech Recognition and SynthesisSpeech and Audio ProcessingVoice and Speech Disorders