# Connected Speech-Based Cognitive Assessment in Chinese and English

arXiv: https://arxiv.org/abs/2406.10272 · HTML: https://mlsanity.com/paper/2406.10272/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [Artificial Intelligence, speech and language processing approaches to monitoring Alzheimer's Disease: a systematic review](https://mlsanity.com/paper/2010.06047/) (2020)
- [Multilingual Alzheimer's Dementia Recognition through Spontaneous Speech: a Signal Processing Grand Challenge](https://mlsanity.com/paper/2301.05562/) (2023)

## Built on by (most cited first, 13 of 13)

- [CogniVoice: Multimodal and Multilingual Fusion Networks for Mild Cognitive Impairment Assessment from Spontaneous Speech](https://mlsanity.com/paper/2407.13660/) (2024)
- [CogniAlign: Word-Level Multimodal Speech Alignment with Gated Cross-Attention for Alzheimer's Detection](https://mlsanity.com/paper/2506.01890/) (2025)
- [Infusing Acoustic Pause Context into Text-Based Dementia Assessment](https://mlsanity.com/paper/2408.15188/) (2024)
- [Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment](https://mlsanity.com/paper/2501.18731/) (2025)
- [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)
- [Enhancing and Exploring Mild Cognitive Impairment Detection with W2V-BERT-2.0](https://mlsanity.com/paper/2501.16201/) (2025)
- [Dementia classification from spontaneous speech using wrapper-based feature selection](https://mlsanity.com/paper/2502.03484/) (2025)
- [Foundation Model-based Evaluation of Neuropsychiatric Disorders: A Lifespan-Inclusive, Multi-Modal, and Multi-Lingual Study](https://mlsanity.com/paper/2512.20948/) (2025)
- [Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach](https://mlsanity.com/paper/2606.05545/) (2026)
- [Fair Cognitive Impairment Detection Through Unlearning](https://mlsanity.com/paper/2606.18571/) (2026)
- [Toward Generalizable Cognitive Impairment Detection with Speech-Based Multimodal Large Language Models](https://mlsanity.com/paper/2607.21496/) (2026)

## Abstract (arXiv)

We present a novel benchmark dataset and prediction tasks for investigating approaches to assess cognitive function through analysis of connected speech. The dataset consists of speech samples and clinical information for speakers of Mandarin Chinese and English with different levels of cognitive impairment as well as individuals with normal cognition. These data have been carefully matched by age and sex by propensity score analysis to ensure balance and representativity in model training. The prediction tasks encompass mild cognitive impairment diagnosis and cognitive test score prediction. This framework was designed to encourage the development of approaches to speech-based cognitive assessment which generalise across languages. We illustrate it by presenting baseline prediction models that employ language-agnostic and comparable features for diagnosis and cognitive test score prediction. The models achieved unweighted average recall was 59.2% in diagnosis, and root mean squared error of 2.89 in score prediction.