Editorial: Alzheimer's Dementia Recognition through Spontaneous Speech

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While a number of studies have investigated speech and language features for the detection of AD and mild cognitive impairment (Fraser et al., 2016), and proposed various signal processing and machine learning methods for this task (Petti et al., 2020), the field still lacks balanced benchmark data against which different approaches can be systematically compared. This Research Topic addresses this issue by exploring the use of speech characteristics for AD recognition using balanced data and shared tasks, such as those provided by the ADReSS Challenges (Luz et al., 2020(Luz et al., , 2021. These tasks have brought together groups working on this active area of research, providing the community with benchmarks for comparison of speech and language approaches to cognitive assessment. Reflecting the multidisciplinary character of the topic, the articles in this collection span three journals: Frontiers of Aging Neuroscience, Frontiers of Computer Science and Frontiers in Psychology.Most papers in this Reseach Topic target two main tasks: AD classification, for distinguishing individuals with AD from healthy controls, and cognitive test score regression, to infer the patient's Mini Mental Status Examination (MMSE) score (Folstein et al., 1975). Of the twenty papers published in this collection, 14 used the ADReSS dataset (Luz et al., 2020), by itself or in combination with other data. The ADReSS dataset is a curated subset of DementiaBank's Pitt Corpus, matched for age and ge...