Alzheimer's Dementia Recognition through Spontaneous Speech

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

News:

Dementia is a category of neurodegenerative diseases that entails a long-term and usually gradual decrease of cognitive functioning. The main risk factor for dementia is age, and therefore its greatest incidence is amongst the elderly. Due to the severity of the situation worldwide, institutions and researchers are investing considerably on dementia prevention and early detection, focusing on disease progression. There is a need for cost-effective and scalable methods for detection of dementia from its most subtle forms, such as the preclinical stage of Subjective Memory Loss (SML), to more severe conditions like Mild Cognitive Impairment (MCI) and Alzheimer's Dementia (AD) itself.

While a number of studies have investigated speech and language features for the detection of Alzheimer's Disease and mild cognitive impairment, and proposed various signal processing and machine learning methods for this prediction task, the field still lacks balanced and standardised data sets on which these different approaches can be systematically compared.

The main objective of the ADReSS challenge is to make available a benchmark dataset of spontaneous speech, which is acoustically pre-processed and balanced in terms of age and gender, defining a shared task through which different approaches to AD recognition in spontaneous speech can be compared. We expect that this challenge will bring together groups working on this active area of research, and provide the community with the very first comprehensive comparison of different approaches to AD recognition using this benchmark dataset.

In sum:

• The ADReSS Challenge will target a difficult automatic prediction problem of societal and medical relevance, namely, the detection of cognitive impairment and Alzheimer's Dementia (AD). To the best of our knowledge, this will be the first such shared-task event focused on AD.
• While a number of researchers have proposed speech processing and natural language procesing approaches to AD recognition through speech, their studies have used different, often unbalanced and acoustically varied data sets, consequently hindering reproducibility and comparability of approaches. The ADReSS Challenge will provide a forum for those different research groups to test their existing methods (or develop novel approaches) on a new shared standardized dataset.
• Th ADReSS Challenge dataset has been carefully selected so as to mitigate common biases often overlooked in evaluations of AD detection methods, including repeated occurrences of speech from the same participant (common in longitudinal datasets), variations in audio quality, and imbalances of gender and age distribution.
• Unlike some tests performed in clinical settings, where short speech samples are collected under controlled conditions, this task focuses AD recognition using spontaneous speech.

## How to participate

1. an AD classification task, where you are required to produce a model to predict the label (AD or non-AD) for a speech session. Your model can use speech data, language data (transcipts are provided), or both.
2. an MMSE score regression task, where you will create a model to infer the subject's Mini Mental Status Examination (MMSE) score based on speech and/or language data.

You will also be expected to submit a paper to INTERSPEECH 2020, describing your approach and results. If your paper is accepted, it will be presented at the conference in the ADReSS special session.

Once you have become a member of DementiaBank, please email us at Fasih.Haider@ed.ac.uk for futher instructions.

## The data set

The DementiaBank directory to which you will gain access will contain only the training data for the ADReSS Challenge. This will consists of four folders of data (full enhanced audio, normalised sub-chunks, transcriptions) as well as two text files with information on age, gender and MMSE scores for participants with and without a diagnosis of AD (cc_meta_data.txt, cd_meta_data.txt). A README file is also included for further details. The composition of the full dataset is shown below:

Age Interval Male Female Male Female
[50, 55) 2 0 2 0
[55, 60) 7 6 7 6
[60, 65) 4 9 4 9
[65, 70) 9 14 9 14
[70, 75) 9 11 9 11
[75, 80) 4 3 4 3
Total 35 43 35 43

Each session was segmented for voice activity using a voice activity detection system based on a signal energy threshold. We set the log energy threshold parameter to 65dB with a maximum duration of 10 seconds per speech segment. The segmented dataset contains 1,955 speech segments from 78 non-AD subjects and 2122 speech segments from 78 AD subjects. The average number of speech segments produced per participant was 24.86 (standard deviationsd= 12.84). Audio volume was normalised across all speech segments to control for variation caused by recording conditions, such as microphone placement.

## Performance Metrics

Task 1 (AD classification) will be evaluated through the following metrics: $\displaystyle \operatorname {Accuracy} = {\frac { TN + TP }{N} }$ and $\displaystyle \operatorname {F_1} = { 2 \frac { \pi \times \rho }{\pi + \rho} }$ where $\displaystyle \operatorname {\pi} = { \frac { TP }{TP + FP} },$ $\displaystyle \operatorname {\rho} = { \frac { TP }{TP + FN} },$ N is the number of patients, TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives and FN the number of false negatives.

Task 2 (MMSE prediction) will be evaluated using the root mean squared error: $\displaystyle \operatorname {RMSE} ={\sqrt {\frac {\sum _{i=1}^{N}({\hat {y}}_{i}-y_{i})^{2}}{N}}}.$ where $\hat{y}$ is the predicted MMSE score amd $y$ is the patient's actual MMSE score.

## Baseline Results

A basic set of baseline results can be found in the paper below. Papers submitted to this Challenge using the ADReSS dataset need to cite it as follows:
1. S. Luz, F. Haider, S. de la Fuente, D. Fromm, and B. MacWhinney. Alzheimer's dementia recognition through spontaneous speech: The ADReSS challenge. In Proceedings of INTERSPEECH 2020, Shanghai, China, 2020. [ bib | http ]

### Test set labels and MMSE scores

The test set prediction targets are now available for download through this link. These could be useful, for instance, if you wish to prepare an extended version of your paper for our Frontiers Special Research Topic on Alzheimer's dementia recognition through spontaneous speech

## Important Dates

• March 15, 2020: test data made available
• March 17, 2020 April 23, 2020: Submission of results opens (period for submision: April 23 to May 8)
• May 8, 2020: Paper submission deadline
• July 24, 2020: Paper acceptance/rejection notification
• October 26-29, 2020: INTERSPEECH'2020, in Shanghai, China.
See other important dates on the INTERSPEECH 2020 website.

## Paper Submission

Please format your paper following the INTERSPEECH 2020 guidelines, and submit it indicating that it is meant for the ADReSS Challenge.

Papers submitted to this Challenge need to cite:

1. S. Luz, F. Haider, S. de la Fuente, D. Fromm, and B. MacWhinney. Alzheimer's dementia recognition through spontaneous speech: The ADReSS challenge. In Proceedings of INTERSPEECH 2020, Shanghai, China, 2020. [ bib | http ]

## Special Issue

Revised and extended versions of the papers accepted for the ADReSS Challenge can also be submitted to a Special Research Topic on Alzheimer's dementia recognition through spontaneous speech jointly hosted by journals Frontiers in Aging Neuroscience, Frontiers in Psychology and Frontiers in Computer Science.