# Detecting cognitive decline using speech only: The ADReSSo Challenge

arXiv: https://arxiv.org/abs/2104.09356 · HTML: https://mlsanity.com/paper/2104.09356/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 36 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge](https://mlsanity.com/paper/2004.06833/) (2020)
- [Artificial Intelligence, speech and language processing approaches to monitoring Alzheimer's Disease: a systematic review](https://mlsanity.com/paper/2010.06047/) (2020)
- [A Method for Analysis of Patient Speech in Dialogue for Dementia Detection](https://mlsanity.com/paper/1811.09919/) (2018)

## Built on by (most cited first, 30 of 81)

- [Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs](https://mlsanity.com/paper/2106.15684/) (2021)
- [Comparing Acoustic-based Approaches for Alzheimer's Disease Detection](https://mlsanity.com/paper/2106.01555/) (2021)
- [Explainable Identification of Dementia from Transcripts using Transformer Networks](https://mlsanity.com/paper/2109.06980/) (2021)
- [Alzheimer's Disease Detection from Spontaneous Speech through Combining Linguistic Complexity and (Dis)Fluency Features with Pretrained Language Models](https://mlsanity.com/paper/2106.08689/) (2021)
- [Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection](https://mlsanity.com/paper/2206.13232/) (2022)
- [Non-Contrastive Self-supervised Learning for Utterance-Level Information Extraction from Speech](https://mlsanity.com/paper/2208.05445/) (2022)
- [LLMs-Healthcare : Current Applications and Challenges of Large Language Models in various Medical Specialties](https://mlsanity.com/paper/2311.12882/) (2023)
- [Exploring linguistic feature and model combination for speech recognition based automatic AD detection](https://mlsanity.com/paper/2206.13758/) (2022)
- [Linguistic Features Extracted by GPT-4 Improve Alzheimer's Disease Detection based on Spontaneous Speech](https://mlsanity.com/paper/2412.15772/) (2024)
- [Data Augmentation for Dementia Detection in Spoken Language](https://mlsanity.com/paper/2206.12879/) (2022)
- [CogniAlign: Word-Level Multimodal Speech Alignment with Gated Cross-Attention for Alzheimer's Detection](https://mlsanity.com/paper/2506.01890/) (2025)
- [Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection](https://mlsanity.com/paper/2210.16539/) (2022)
- [A Multimodal Approach for Dementia Detection from Spontaneous Speech with Tensor Fusion Layer](https://mlsanity.com/paper/2211.04368/) (2022)
- [Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speech](https://mlsanity.com/paper/2305.16406/) (2023)
- [Anonymising Elderly and Pathological Speech: Voice Conversion Using DDSP and Query-by-Example](https://mlsanity.com/paper/2410.15500/) (2024)
- [A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection](https://mlsanity.com/paper/2410.19898/) (2024)
- [CognoSpeak: an automatic, remote assessment of early cognitive decline in real-world conversational speech](https://mlsanity.com/paper/2501.05755/) (2025)
- [Evaluating Spoken Language as a Biomarker for Automated Screening of Cognitive Impairment](https://mlsanity.com/paper/2501.18731/) (2025)
- [Evaluating Picture Description Speech for Dementia Detection using Image-text Alignment](https://mlsanity.com/paper/2308.07933/) (2023)
- [Transferring speech-generic and depression-specific knowledge for Alzheimer's disease detection](https://mlsanity.com/paper/2310.04358/) (2023)
- [A Digital Language Coherence Marker for Monitoring Dementia](https://mlsanity.com/paper/2310.09623/) (2023)
- [Reformulating NLP tasks to Capture Longitudinal Manifestation of Language Disorders in People with Dementia](https://mlsanity.com/paper/2310.09897/) (2023)
- [The Unreliability of Acoustic Systems in Alzheimer's Speech Datasets with Heterogeneous Recording Conditions](https://mlsanity.com/paper/2409.12170/) (2024)
- [A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunities](https://mlsanity.com/paper/2409.19737/) (2024)
- [Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning](https://mlsanity.com/paper/2111.07454/) (2021)
- [Going Beyond the Cookie Theft Picture Test: Detecting Cognitive Impairments using Acoustic Features](https://mlsanity.com/paper/2206.05018/) (2022)
- [Performance Assessment of ChatGPT vs Bard in Detecting Alzheimer's Dementia](https://mlsanity.com/paper/2402.01751/) (2024)
- [Semi-Supervised Cognitive State Classification from Speech with Multi-View Pseudo-Labeling](https://mlsanity.com/paper/2409.16937/) (2024)
- [DiDOTS: Knowledge Distillation from Large-Language-Models for Dementia Obfuscation in Transcribed Speech](https://mlsanity.com/paper/2410.04188/) (2024)
- [DECT: Harnessing LLM-assisted Fine-Grained Linguistic Knowledge and Label-Switched and Label-Preserved Data Generation for Diagnosis of Alzheimer's Disease](https://mlsanity.com/paper/2502.04394/) (2025)

## Abstract (arXiv)

Building on the success of the ADReSS Challenge at Interspeech 2020, which attracted the participation of 34 teams from across the world, the ADReSSo Challenge targets three difficult automatic prediction problems of societal and medical relevance, namely: detection of Alzheimer's Dementia, inference of cognitive testing scores, and prediction of cognitive decline. This paper presents these prediction tasks in detail, describes the datasets used, and reports the results of the baseline classification and regression models we developed for each task. A combination of acoustic and linguistic features extracted directly from audio recordings, without human intervention, yielded a baseline accuracy of 78.87% for the AD classification task, an MMSE prediction root mean squared (RMSE) error of 5.28, and 68.75% accuracy for the cognitive decline prediction task.