Cross-lingual detection of mild cognitive impairment based on temporal parameters of spontaneous speech

Abstract In this paper, we seek to automatically identify Hungarian patients suffering from mild cognitive impairment (MCI) or mild Alzheimer’s Disease (mAD) based on their speech transcripts, focusing only on linguistic features. In addition to the features examined in our earlier study, we introduce syntactic, semantic and pragmatic features of spontaneous speech that might affect the detection of dementia. In order to ascertain the most useful features for distinguishing healthy controls, MCI patients and mAD patients, we will carry out a statistical analysis of the data and investigate the significance level of the extracted features among various speaker group pairs and for various speaking tasks. In the second part of the paper, we use this rich feature set as a basis for an effective discrimination among the three speaker groups. In our machine learning experiments, we will analyze the efficacy of each feature group separately. Our model which uses all the features achieves competitive scores, either with or without demographic information (3-class accuracy values: 68–70%, 2-class accuracy values: 77.3–80%). We also analyze how different data recording scenarios affect linguistic features and how they can be productively used when distinguishing MCI patients from healthy controls.

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Detecting Mild Cognitive Impairment from Spontaneous Speech by Correlation-Based Phonetic Feature Selection

10.21437/interspeech.2016-384 ◽

2016 ◽

Cited By ~ 12

Author(s):

Gábor Gosztolya ◽

László Tóth ◽

Tamás Grósz ◽

Veronika Vincze ◽

Ildikó Hoffmann ◽

...

Keyword(s):

Feature Selection ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Spontaneous Speech ◽

Phonetic Feature

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Automatic detection of Alzheimer’s disease and mild cognitive impairment from spontaneous speech collected during tablet‐based interviews: A preliminary result

Alzheimer s & Dementia ◽

10.1002/alz.042900 ◽

2020 ◽

Vol 16 (S6) ◽

Author(s):

Kaoru Shinkawa ◽

Akihiro Kosugi ◽

Masatomo Kobayashi ◽

Masafumi Nishimura ◽

Miyuki Nemoto ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Automatic Detection ◽

Spontaneous Speech

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P3-106: Spontaneous speech rate in patients with mild cognitive impairment and early stage Alzheimer's disease

Alzheimer s & Dementia ◽

10.1016/j.jalz.2010.05.1603 ◽

2010 ◽

Vol 6 ◽

pp. S481-S481 ◽

Cited By ~ 1

Author(s):

Seol-Heui Han ◽

Minyoung Kim ◽

Sook Hui Kim ◽

Hyun Seok Kang

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Early Stage ◽

Speech Rate ◽

Spontaneous Speech

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A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech

Current Alzheimer Research ◽

10.2174/1567205014666171121114930 ◽

2018 ◽

Vol 15 (2) ◽

pp. 130-138 ◽

Cited By ~ 34

Author(s):

Laszlo Toth ◽

Ildiko Hoffmann ◽

Gabor Gosztolya ◽

Veronika Vincze ◽

Greta Szatloczki ◽

...

Keyword(s):

Machine Learning ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Speech Recognition ◽

Screening Method ◽

Automatic Detection ◽

Spontaneous Speech ◽

Machine Learning Algorithms ◽

Control Group ◽

Acoustic Parameters

Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. Methods: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. Results: The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process – that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. Conclusion: The temporal analysis of spontaneous speech can be exploited in implementing a new, automatic detection-based tool for screening MCI for the community.

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Automatic detection of mild cognitive impairment from spontaneous speech using ASR

10.21437/interspeech.2015-568 ◽

2015 ◽

Author(s):

László Tóth ◽

Gábor Gosztolya ◽

Veronika Vincze ◽

Ildikó Hoffmann ◽

Gréta Szatlóczki ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Automatic Detection ◽

Spontaneous Speech

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Usefulness of Spontaneous Speech Analysis Scales in Patients with Mild Cognitive Impairment and Dementia of Alzheimer’s Type

Communication Sciences & Disorders ◽

10.12963/csd.16310 ◽

2016 ◽

Vol 21 (2) ◽

pp. 284-294 ◽

Cited By ~ 5

Author(s):

Cheon Jin ◽

Hyunjoo Choi ◽

Jun-Young Lee

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Spontaneous Speech ◽

Speech Analysis ◽

Dementia Of Alzheimer’S Type

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Spontaneous Speech of Patients With Dementia of the Alzheimer Type and Mild Cognitive Impairment

International Psychogeriatrics ◽

10.1017/s1041610201007682 ◽

2001 ◽

Vol 13 (3) ◽

pp. 289-298 ◽

Cited By ~ 49

Author(s):

Tom Bschor ◽

Klaus-Peter Kühl ◽

Friedel M. Reischies

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Spontaneous Speech ◽

Alzheimer Type ◽

Boston Naming Test ◽

Global Deterioration Scale ◽

Language Tests ◽

Picture Description ◽

Description Task ◽

Naming Test

This article discusses the potential of three assessments of language function in the diagnosis of Alzheimer-type dementia (DAT). A total of 115 patients (mean age 65.9 years) attending a memory clinic were assessed using three language tests: a picture description task (Boston Cookie-Theft picture), the Boston Naming Test, and a semantic and phonemic word fluency measure. Results of these assessments were compared with those of clinical diagnosis including the Global Deterioration Scale (GDS). The patients were classified by ICD-10 diagnosis and GDS stage as without cognitive impairment (n = 40), mild cognitive impairment (n = 34), mild DAT (n = 21), and moderate to severe DAT (n = 20). Hypotheses were (a) that the complex task of a picture description could more readily identify language disturbances than specific language tests and that (b) examination of spontaneous speech could help to identify patients with even mild forms of DAT. In the picture description task, all diagnostic groups produced an equal number of words. However, patients with mild or moderate to severe DAT described significantly fewer objects and persons, actions, features, and localizations than patients without or with mild cognitive impairment. Persons with mild cognitive impairment had results similar to those without cognitive impairment. The Boston Naming Test and both fluency measures were superior to the picture description task in differentiating the diagnostic groups. In sum, both hypotheses had to be rejected. Our results confirm that DAT patients have distinct semantic speech disturbances whereas they are not impaired in the amount of produced speech.

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