A Dual-Stage Time-Context Network for Speech-Based Alzheimer's Disease Detection

TL;DR

This paper proposes a Dual-Stage Time-Context Network that effectively combines local acoustic features and global conversational context for improved speech-based Alzheimer's disease detection, outperforming existing models on benchmark data.

Contribution

Introduction of DSTC-Net, a novel model that integrates local and global speech features using attention mechanisms for early AD detection.

Findings

Achieves 83.10% accuracy on ADReSSo dataset.

Outperforms state-of-the-art models in AD detection.

Effectively models long-duration speech for clinical diagnosis.

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that leads to irreversible cognitive decline in memory and communication. Early detection of AD through speech analysis is crucial for delaying disease progression. However, existing methods mainly use pre-trained acoustic models for feature extraction but have limited ability to model both local and global patterns in long-duration speech. In this letter, we introduce a Dual-Stage Time-Context Network (DSTC-Net) for speech-based AD detection, integrating local acoustic features with global conversational context in long-duration recordings.We first partition each long-duration recording into fixed-length segments to reduce computational overhead and preserve local temporal details.Next, we feed these segments into an Intra-Segment Temporal Attention (ISTA) module, where a bidirectional Long Short-Term Memory (BiLSTM) network with frame-level attention extracts enhanced local features.Subsequently, a Cross-Segment Context Attention (CSCA) module applies convolution-based context modeling and adaptive attention to unify global patterns across all segments.Extensive experiments on the ADReSSo dataset show that our DSTC-Net outperforms state-of-the-art models, reaching 83.10% accuracy and 83.15% F1.