# Temporal-Aware Iterative Speech Model for Dementia Detection

arXiv: https://arxiv.org/abs/2510.00030 · HTML: https://mlsanity.com/paper/2510.00030/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow](https://mlsanity.com/paper/2203.16896/) (2022)
- [Detecting cognitive decline using speech only: The ADReSSo Challenge](https://mlsanity.com/paper/2104.09356/) (2021)
- [Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition](https://mlsanity.com/paper/2407.04675/) (2024)
- [Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech](https://mlsanity.com/paper/2305.16406/) (2023)
- [Infusing Acoustic Pause Context into Text-Based Dementia Assessment](https://mlsanity.com/paper/2408.15188/) (2024)
- [Generative Context-aware Fine-tuning of Self-supervised Speech Models](https://mlsanity.com/paper/2312.09895/) (2023)
- [Prosody-Driven Privacy-Preserving Dementia Detection](https://mlsanity.com/paper/2407.03470/) (2024)
- [A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis](https://mlsanity.com/paper/2109.01537/) (2021)
- [Profiling Patient Transcript Using Large Language Model Reasoning Augmentation for Alzheimer's Disease Detection](https://mlsanity.com/paper/2409.12541/) (2024)
- [Dementia Detection using Multi-modal Methods on Audio Data](https://mlsanity.com/paper/2501.00465/) (2024)

## Built on by (most cited first, 2 of 2)

- [ASR-Agnostic Multimodal Spectrotemporal Modeling for Early Dementia Detection](https://mlsanity.com/paper/2606.30646/) (2026)
- [CCMAN: Cognitive Instability-Aware Cross-Modal Attention Network for Interpretable Temporal Biomarkers of Verbal Fluency Speech](https://mlsanity.com/paper/2609.14764/) (2026)

## Abstract (arXiv)

Deep learning systems often struggle with processing long sequences, where computational complexity can become a bottleneck. Current methods for automated dementia detection using speech frequently rely on static, time-agnostic features or aggregated linguistic content, lacking the flexibility to model the subtle, progressive deterioration inherent in speech production. These approaches often miss the dynamic temporal patterns that are critical early indicators of cognitive decline. In this paper, we introduce TAI-Speech, a Temporal Aware Iterative framework that dynamically models spontaneous speech for dementia detection. The flexibility of our method is demonstrated through two key innovations: 1) Optical Flow-inspired Iterative Refinement: By treating spectrograms as sequential frames, this component uses a convolutional GRU to capture the fine-grained, frame-to-frame evolution of acoustic features. 2) Cross-Attention Based Prosodic Alignment: This component dynamically aligns spectral features with prosodic patterns, such as pitch and pauses, to create a richer representation of speech production deficits linked to functional decline (IADL). TAI-Speech adaptively models the temporal evolution of each utterance, enhancing the detection of cognitive markers. Experimental results on the DementiaBank dataset show that TAI-Speech achieves a strong AUC of 0.839 and 80.6% accuracy, outperforming text-based baselines without relying on ASR. Our work provides a more flexible and robust solution for automated cognitive assessment, operating directly on the dynamics of raw audio.