标题： MoTAS：从TTS增强语音中基于Moe的特征选择用于增强的多模态阿尔茨海默病早期筛查 显示英文标题

标题： MoTAS: MoE-Guided Feature Selection from TTS-Augmented Speech for Enhanced Multimodal Alzheimer's Early Screening

摘要： 早期通过语音对阿尔茨海默病（AD）进行筛查是一种有前景的非侵入性方法。 然而，数据有限以及缺乏细粒度、自适应的特征选择等挑战常常阻碍性能。 为解决这些问题，我们提出了MoTAS，一个旨在提高AD筛查效率的鲁棒框架。 MoTAS利用文本转语音（TTS）增强来增加数据量，并采用专家混合（MoE）机制来改进多模态特征选择，共同增强模型泛化能力。 该过程首先通过自动语音识别（ASR）获得准确的转录文本。 然后使用TTS合成语音以丰富数据集。 在提取声学和文本嵌入后，MoE机制动态选择最有信息量的特征，优化特征融合以提高分类效果。 在ADReSSo数据集上评估，MoTAS达到了领先的准确率85.71%，优于现有基线方法。 消融研究进一步验证了TTS增强和MoE在提升分类性能中的单独贡献。 这些发现突显了MoTAS在实际AD筛查场景中的实用价值，特别是在数据有限的情况下。 显示英文摘要

摘要： Early screening for Alzheimer's Disease (AD) through speech presents a promising non-invasive approach. However, challenges such as limited data and the lack of fine-grained, adaptive feature selection often hinder performance. To address these issues, we propose MoTAS, a robust framework designed to enhance AD screening efficiency. MoTAS leverages Text-to-Speech (TTS) augmentation to increase data volume and employs a Mixture of Experts (MoE) mechanism to improve multimodal feature selection, jointly enhancing model generalization. The process begins with automatic speech recognition (ASR) to obtain accurate transcriptions. TTS is then used to synthesize speech that enriches the dataset. After extracting acoustic and text embeddings, the MoE mechanism dynamically selects the most informative features, optimizing feature fusion for improved classification. Evaluated on the ADReSSo dataset, MoTAS achieves a leading accuracy of 85.71\%, outperforming existing baselines. Ablation studies further validate the individual contributions of TTS augmentation and MoE in boosting classification performance. These findings highlight the practical value of MoTAS in real-world AD screening scenarios, particularly in data-limited settings.