Research Topic

Alzheimer's Dementia Recognition through Spontaneous Speech

About this Research Topic

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 in dementia prevention and early detection, focusing on disease progression. There is a need for cost-effective and scalable methods for the detection of dementia from its most subtle forms, such as the pre-clinical stage of Subjective Cognitive Impairment (SCI), 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 have proposed various signal processing and machine learning methods for this prediction task, the field still lacks balanced and standardized data sets on which these different approaches can be systematically compared.

The main objective of this Research Topic is to explore the use of speech characteristics for AD recognition using standardized data sets. We expect that this Topic 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 the speech information.


This Research Topic draws on the ADReSS Challenge, held as part of INTERSPEECH 2020, and targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of cognitive impairment and Alzheimer's Dementia (AD). The ADReSS challenge centred around two tasks, both based on speech and language data:

• AD classification, for distinguishing individuals with AD from age and gender matched healthy controls.

• MMSE score regression task, where the authors will create a model to infer the subject's Mini Mental Status Examination (MMSE) score.

This Research Topic invites submissions describing work related, but not limited to, these prediction tasks and the ADReSS Challenge, focused on the use of AI methods in AD prediction based on speech and language.

We would like to acknowledge that Sofia De La Fuente García has acted as coordinator and has contributed to the preparation of the proposal for this Research Topic.


Keywords: Speech Technology, Affective Computing, Prosodic Analysis, Machine Learning, Mental Health monitoring


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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 in dementia prevention and early detection, focusing on disease progression. There is a need for cost-effective and scalable methods for the detection of dementia from its most subtle forms, such as the pre-clinical stage of Subjective Cognitive Impairment (SCI), 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 have proposed various signal processing and machine learning methods for this prediction task, the field still lacks balanced and standardized data sets on which these different approaches can be systematically compared.

The main objective of this Research Topic is to explore the use of speech characteristics for AD recognition using standardized data sets. We expect that this Topic 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 the speech information.


This Research Topic draws on the ADReSS Challenge, held as part of INTERSPEECH 2020, and targets a difficult automatic prediction problem of societal and medical relevance, namely, the detection of cognitive impairment and Alzheimer's Dementia (AD). The ADReSS challenge centred around two tasks, both based on speech and language data:

• AD classification, for distinguishing individuals with AD from age and gender matched healthy controls.

• MMSE score regression task, where the authors will create a model to infer the subject's Mini Mental Status Examination (MMSE) score.

This Research Topic invites submissions describing work related, but not limited to, these prediction tasks and the ADReSS Challenge, focused on the use of AI methods in AD prediction based on speech and language.

We would like to acknowledge that Sofia De La Fuente García has acted as coordinator and has contributed to the preparation of the proposal for this Research Topic.


Keywords: Speech Technology, Affective Computing, Prosodic Analysis, Machine Learning, Mental Health monitoring


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

15 July 2020 Abstract
31 October 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

15 July 2020 Abstract
31 October 2020 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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