Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge (2004.06833v3)

Abstract: The ADReSS Challenge at INTERSPEECH 2020 defines a shared task through which different approaches to the automated recognition of Alzheimer's dementia based on spontaneous speech can be compared. ADReSS provides researchers with a benchmark speech dataset which has been acoustically pre-processed and balanced in terms of age and gender, defining two cognitive assessment tasks, namely: the Alzheimer's speech classification task and the neuropsychological score regression task. In the Alzheimer's speech classification task, ADReSS challenge participants create models for classifying speech as dementia or healthy control speech. In the the neuropsychological score regression task, participants create models to predict mini-mental state examination scores. This paper describes the ADReSS Challenge in detail and presents a baseline for both tasks, including feature extraction procedures and results for classification and regression models. ADReSS aims to provide the speech and language Alzheimer's research community with a platform for comprehensive methodological comparisons. This will hopefully contribute to addressing the lack of standardisation that currently affects the field and shed light on avenues for future research and clinical applicability.

Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge

The paper "Alzheimer's Dementia Recognition through Spontaneous Speech: The ADReSS Challenge" delineates an initiative to standardize and advance methods for the automated recognition of Alzheimer's dementia by leveraging spontaneous speech datasets. Alzheimer's Disease (AD) is a leading cause of dementia, with substantial global health implications due to its prevalence among the aging population. Early detection methods that are both cost-effective and scalable are critically required. The ADReSS Challenge, held at INTERSPEECH 2020, provides a focused platform for evaluating different approaches by offering a benchmark dataset and defining specific predictive tasks.

ADReSS Challenge Overview

The ADReSS Challenge addresses previous limitations in research pertinent to Alzheimer's speech analysis, such as the lack of standardized and balanced datasets that allow methodological comparisons. It introduces a benchmark dataset derived from speech recordings and transcripts, ensuring they are balanced in terms of age and gender and acoustically pre-processed. Two main cognitive tasks are defined: classifying speech into categories of dementia versus healthy controls (AD classification task), and predicting neuropsychological scores such as the Mini-Mental State Examination (MMSE) from speech data (MMSE prediction task).

Methodology and Dataset Details

The Challenge facilitates two prediction tasks:

• AD Classification Task: Researchers are prompted to use speech and linguistic data for binary classification purposes—distinguishing between speech samples from AD patients and non-AD controls.
• MMSE Prediction Task: This encompasses developing regression models to predict MMSE scores based on the linguistic and acoustic features extracted from speech recordings.

The dataset employed within the Challenge originated from spoken picture descriptions and underwent meticulous acoustic enhancements involving noise removal and normalization. This dataset encompasses meticulously segmented and timestamped transcriptions annotated using the CHAT coding system.

Acoustic and Linguistic Feature Extraction

A critical component of the Challenge involves the extraction and analysis of diverse acoustic and linguistic features, utilizing tools such as the openSMILE toolkit. Feature sets like emobase, ComParE, eGeMAPS, and MRCG functionals are employed, capturing dimensions from mel-frequency cepstral coefficients to multi-resolution cochleagram features. Combined with minimal linguistic statistics derived from transcripts, these features underpin the development of machine learning models for task execution.

Experimental Results and Baseline Models

Baseline models are proposed for both tasks, with a range of machine learning approaches explored, including Linear Discriminant Analysis (LDA), Decision Trees (DT), and Support Vector Machines (SVMs). The paper reveals variations in model performance across different acoustic and linguistic feature sets. For example, linguistic features yielded superior accuracy in classification tasks, emphasizing the value of manually transcribed data when compared to purely acoustic features. These results underscore the complexity of developing robust models purely from spontaneous speech.

Implications and Future Prospects

The ADReSS Challenge presents an important step towards addressing standardization issues in AD speech analysis and opens avenues for future research. By facilitating a shared dataset and benchmark framework, it enhances the reproducibility and comparability across methodologies. This initiative is poised to engender refinements in detection algorithms and might prompt the implementation of enhanced pre-processing and feature extraction techniques. Such advances could potentially lead to scalable solutions for early dementia detection, fostering clinical integration and enhancing patient outcomes.

The results from this Challenge can instigate broader discussions about methodological frameworks and data homogeneity for leveraging spontaneous speech in neurological disorder contexts beyond Alzheimer's disease. It is hoped that these efforts will aid in bridging the gap from research to diagnosis, ultimately contributing to improved management strategies for cognitive decline disorders.