Abstract
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.
Abstract (translated)
深度学习系统在处理长序列时常常遇到挑战，计算复杂性往往成为瓶颈。目前用于自动检测痴呆症的语音方法通常依赖于静态、时间无关特征或聚合的语言内容，缺乏建模言语生产中细微且渐进式退化的灵活性。这些方法经常会错过动态的时间模式，而这些模式是认知衰退早期的重要指标。在本文中，我们介绍了一种新的框架TAI-Speech（Temporal Aware Iterative），它能够动态地对自发语音进行痴呆症检测。通过两个关键创新展示了我们的方法的灵活性： 1) **光学流启发的迭代优化**：该组件将光谱图视为连续帧，并使用卷积GRU来捕捉声学特征在帧到帧之间的细微演变。 2) **基于交叉注意力的韵律对齐**：此部分动态地将频谱特征与音高和停顿等韵律模式进行对齐，以创建更丰富的言语产出缺陷表示，这些缺陷与功能衰退（IADL）相关联。 TAI-Speech能够适应性地建模每个话语的时间演变，从而增强认知标志的检测能力。在DementiaBank数据集上的实验结果显示，TAI-Speech取得了AUC为0.839和准确率为80.6%的良好性能，在不依赖于ASR（自动语音识别）的情况下超越了基于文本的方法基准。我们的工作提供了一种更为灵活且稳健的自动化认知评估解决方案，直接在原始音频的动力学上进行操作。
URL
https://arxiv.org/abs/2510.00030
