# Cognitive Insights Across Languages: Enhancing Multimodal Interview Analysis

arXiv: https://arxiv.org/abs/2406.07542 · HTML: https://mlsanity.com/paper/2406.07542/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection](https://mlsanity.com/paper/2008.01551/) (2020)
- [Multimodal Inductive Transfer Learning for Detection of Alzheimer's Dementia and its Severity](https://mlsanity.com/paper/2009.00700/) (2020)
- [Longitudinal Speech Biomarkers for Automated Alzheimer's Detection](https://mlsanity.com/paper/2111.11859/) (2021)

## Built on by (most cited first, 4 of 4)

- [Zero-Shot Cognitive Impairment Detection from Speech Using AudioLLM](https://mlsanity.com/paper/2506.17351/) (2025)
- [Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques](https://mlsanity.com/paper/2410.18972/) (2024)
- [Unveil Multi-Picture Descriptions for Multilingual Mild Cognitive Impairment Detection via Contrastive Learning](https://mlsanity.com/paper/2505.17067/) (2025)
- [Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models](https://mlsanity.com/paper/2607.21496/) (2026)

## Abstract (arXiv)

Cognitive decline is a natural process that occurs as individuals age. Early diagnosis of anomalous decline is crucial for initiating professional treatment that can enhance the quality of life of those affected. To address this issue, we propose a multimodal model capable of predicting Mild Cognitive Impairment and cognitive scores. The TAUKADIAL dataset is used to conduct the evaluation, which comprises audio recordings of clinical interviews. The proposed model demonstrates the ability to transcribe and differentiate between languages used in the interviews. Subsequently, the model extracts audio and text features, combining them into a multimodal architecture to achieve robust and generalized results. Our approach involves in-depth research to implement various features obtained from the proposed modalities.