This paper presents the Neural Harmony team’s submission to Task 1 (Person Identification) of the ICASSP 2025 EEG-Music Emotion Recognition Challenge, which aims to identify the subject from a given EEG segment. To enhance performance, we propose a novel architecture incorporating the Multiscale ConvBlock and integrating attention mechanisms with convolutional networks. We also reprocessed the data and trained multiple models with different train-validation splits, which were ensembled during testing to further improve robustness. Our final results on the test data exceed the challenge baseline, achieving 100% accuracy in Person Identification. Additionally, unseen subjects were introduced to evaluate the model’s generalization ability, and the results confirm the model’s strong adaptability to new subjects.

Recent advances in automatic speech recognition (ASR) technology have boosted the viability of fully automated Alzheimer’s disease (AD) detection via ASR transcripts. However, there is a lack of understanding of how ASR errors affect the performance of AD detection. This paper addresses that gap. First, we fine-tune 18 ASR models on three datasets from DementiaBank, generating 36 ASR transcripts on the ADReSS dataset (18 from original and 18 from fine-tuned ASR models). We then employ two AD detection methods using either ASR or manual transcripts: fine-tuning four large language models (LLMs) and fusing LLMs with pre-trained language models (PLMs). The results show that certain ASR transcripts outperform manual transcripts, suggesting that ASR errors provide valuable clues for AD detection. Finally, we conduct an interpretability study, including linguistic and SHapley Additive exPlanations (SHAP) analyses. This study reveals that greater word distribution differences between AD and healthy control (HC) groups in ASR transcripts may be linked to these valuable clues. This paper highlights the potential of ASR as a powerful tool for developing fully automated AD detection systems.