# Cross-lingual Alzheimer's Disease detection based on paralinguistic and pre-trained features

arXiv: https://arxiv.org/abs/2303.07650 · HTML: https://mlsanity.com/paper/2303.07650/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (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, 8 of 8)

- [CogniVoice: Multimodal and Multilingual Fusion Networks for Mild Cognitive Impairment Assessment from Spontaneous Speech](https://mlsanity.com/paper/2407.13660/) (2024)
- [Transferring speech-generic and depression-specific knowledge for Alzheimer's disease detection](https://mlsanity.com/paper/2310.04358/) (2023)
- [HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech](https://mlsanity.com/paper/2405.03952/) (2024)
- [MultiConAD: A Unified Multilingual Conversational Dataset for Early Alzheimer's Detection](https://mlsanity.com/paper/2502.19208/) (2025)
- [The order in speech disorder: a scoping review of state of the art machine learning methods for clinical speech classification](https://mlsanity.com/paper/2503.04802/) (2025)
- [Foundation Model-based Evaluation of Neuropsychiatric Disorders: A Lifespan-Inclusive, Multi-Modal, and Multi-Lingual Study](https://mlsanity.com/paper/2512.20948/) (2025)
- [Cross-Scale Transfer Learning for Depression Severity Prediction: From PHQ-8 to HAMD-17 Across Languages and Clinical Paradigms](https://mlsanity.com/paper/2609.28430/) (2026)
- [Explainable and Generalisable LLM-based Cognitive Decline Detection with Spontaneous Speech](https://mlsanity.com/paper/2609.34217/) (2026)

## Abstract (arXiv)

We present our submission to the ICASSP-SPGC-2023 ADReSS-M Challenge Task, which aims to investigate which acoustic features can be generalized and transferred across languages for Alzheimer's Disease (AD) prediction. The challenge consists of two tasks: one is to classify the speech of AD patients and healthy individuals, and the other is to infer Mini Mental State Examination (MMSE) score based on speech only. The difficulty is mainly embodied in the mismatch of the dataset, in which the training set is in English while the test set is in Greek. We extract paralinguistic features using openSmile toolkit and acoustic features using XLSR-53. In addition, we extract linguistic features after transcribing the speech into text. These features are used as indicators for AD detection in our method. Our method achieves an accuracy of 69.6% on the classification task and a root mean squared error (RMSE) of 4.788 on the regression task. The results show that our proposed method is expected to achieve automatic multilingual Alzheimer's Disease detection through spontaneous speech.