Speech rhythm alterations in Spanish-speaking individuals with Alzheimer’s disease

Sánchez, Francisco Martínez; Meilán, Juan José García; Vera-Ferrándiz, Juan A.; Carro, Juan; Pujante-Valverde, Isabel M; Ivanova, Olga; González, Nuria Carcavilla

doi:10.1080/13825585.2016.1220487

Cited by 26 publications

(19 citation statements)

References 67 publications

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“…On the contrary, the speech rhythm seems to be rather preserved in the PGs included in our study, while in a study including eAD patients (MMSE > 24), a high variability of syllabic interval was reported (Martínez-Sánchez et al, 2017 ).…”

Section: Discussioncontrasting

confidence: 77%

Speech Analysis by Natural Language Processing Techniques: A Possible Tool for Very Early Detection of Cognitive Decline?

Beltrami

Gagliardi

Favretti

et al. 2018

Front. Aging Neurosci.

100

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Background: The discovery of early, non-invasive biomarkers for the identification of “preclinical” or “pre-symptomatic” Alzheimer's disease and other dementias is a key issue in the field, especially for research purposes, the design of preventive clinical trials, and drafting population-based health care policies. Complex behaviors are natural candidates for this. In particular, recent studies have suggested that speech alterations might be one of the earliest signs of cognitive decline, frequently noticeable years before other cognitive deficits become apparent. Traditional neuropsychological language tests provide ambiguous results in this context. In contrast, the analysis of spoken language productions by Natural Language Processing (NLP) techniques can pinpoint language modifications in potential patients. This interdisciplinary study aimed at using NLP to identify early linguistic signs of cognitive decline in a population of elderly individuals.Methods: We enrolled 96 participants (age range 50–75): 48 healthy controls (CG) and 48 cognitively impaired participants: 16 participants with single domain amnestic Mild Cognitive Impairment (aMCI), 16 with multiple domain MCI (mdMCI) and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening composed by MMSE, MoCA, GPCog, CDT, and verbal fluency (phonemic and semantic). The spontaneous speech during three tasks (describing a complex picture, a typical working day and recalling a last remembered dream) was then recorded, transcribed and annotated at various linguistic levels. A multidimensional parameter computation was performed by a quantitative analysis of spoken texts, computing rhythmic, acoustic, lexical, morpho-syntactic, and syntactic features.Results: Neuropsychological tests showed significant differences between controls and mdMCI, and between controls and eD participants; GPCog, MoCA, PF, and SF also discriminated between controls and aMCI. In the linguistic experiments, a number of features regarding lexical, acoustic and syntactic aspects were significant in differentiating between mdMCI, eD, and CG (non-parametric statistical analysis). Some features, mainly in the acoustic domain also discriminated between CG and aMCI.Conclusions: Linguistic features of spontaneous speech transcribed and analyzed by NLP techniques show significant differences between controls and pathological states (not only eD but also MCI) and seems to be a promising approach for the identification of preclinical stages of dementia. Long duration follow-up studies are needed to confirm this assumption.

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Section: Discussioncontrasting

confidence: 77%

Speech Analysis by Natural Language Processing Techniques: A Possible Tool for Very Early Detection of Cognitive Decline?

Beltrami

Gagliardi

Favretti

et al. 2018

Front. Aging Neurosci.

100

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“…Each row corresponds to the filter response of one recording. As described in [32], the increase in the power of low frequency ranges can be a result of cognitive decline. The high values and main differences are concentrated in the first several filters for the three tasks.…”

Section: Filter Analysismentioning

confidence: 99%

Acoustic Feature Extraction with Interpretable Deep Neural Network for Neurodegenerative Related Disorder Classification

Pan¹,

Mirheidari²,

Tu³

et al. 2020

Interspeech 2020

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Speech-based automatic approaches for detecting neurodegenerative disorders (ND) and mild cognitive impairment (MCI) have received more attention recently due to being non-invasive and potentially more sensitive than current pen-and-paper tests. The performance of such systems is highly dependent on the choice of features in the classification pipeline. In particular for acoustic features, arriving at a consensus for a best feature set has proven challenging. This paper explores using deep neural network for extracting features directly from the speech signal as a solution to this. Compared with hand-crafted features, more information is present in the raw waveform, but the feature extraction process becomes more complex and less interpretable which is often undesirable in medical domains. Using a Sinc-Net as a first layer allows for some analysis of learned features. We propose and evaluate the Sinc-CLA (with SincNet, Convolutional, Long Short-Term Memory and Attention layers) as a task-driven acoustic feature extractor for classifying MCI, ND and healthy controls (HC). Experiments are carried out on an inhouse dataset. Compared with the popular hand-crafted feature sets, the learned task-driven features achieve a superior classification accuracy. The filters of the SincNet is inspected and acoustic differences between HC, MCI and ND are found.

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“…La alteración del lenguaje es una de las manifestaciones más destacadas y tempranas de la Enfermedad de Alzheimer (Schröder, Wendelstein & Felder, 2010). Las investigaciones más recientes han demostrado que el deterioro del lenguaje en EA es integral y afecta todos los niveles del sistema lingüístico y sus manifestaciones: la fonética segmental y suprasegmental (Mesulman, Rogalski, Wieneke, Hurley, Geula, Bigio, Thompson & Weintraub, 2014;Martínez-Sánchez, Meilán, Vera-Ferrandiz, Carro, Pujante-Valverde, Ivanova & Carcavilla, 2017); el nivel léxico y el nivel semántico (Szatloczki, Hoffmann, Vincze, Kálmán & Pakaski, 2015;Lima Silagi et al, 2015); la gramática y la sintaxis (Ahmed, de Jager, Haigh & Garrard, 2012;Ivanova, 2015); así como la construcción del discurso (Pistono, Jucla, Barbeau, Saint-Aubet, Lemesle, Calvet, Köpke, Puel & Pariente, 2016). Dentro de este cuadro de afectación lingüística compleja, las alteraciones léxico-semánticas ocupan un lugar destacado, tanto por su mayor evidencialidad en la expresión verbal de los hablantes como por su papel central en la caracterización del lenguaje en EA.…”

Section: Marco Teóricounclassified

La habilidad léxico-semántica en la Enfermedad de Alzheimer: Un estudio de la fluidez verbal con categorías semánticas

Ivanova

Meilán²,

Martínez-Nicolás

et al. 2020

Rev. signos

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Language impairments in Alzheimer's disease (AD) primarily affect lexical and semantic levels, which significantly depend on the speaker's memory state. Qualitative shifts in semantic memory are due to neurodegenerative processes underlying dementia and give rise to anomia, or the speaker's inability to access and retrieve lexical units and their conceptual backgrounds. In this paper we aim at studying cognitive and semantic features of anomic deficit in AD and exploring the possibility to apply them in tests for early detection of dementia. For that purpose, we analyze the results from our experimental version of a classical test on semantic verbal fluency (SemVF) with nonpathological aged persons, persons with Mild Cognitive Impairment and persons with AD. The experimental version of the test introduces a division into four 15-minute intervals, in order to find out which processes of semantic access, either automatic or controlled, are impaired in different cognitive states of the elderly. Our results show a correlation between the cognitive state and the lexical-semantic ability of the speaker. Furthermore, they highlight, on the one hand, that the duration of the SemVF test should not be less than 60 seconds, with internal division into 4 15-second intervals, and, on the other hand, that some semantic categories-colours and fruit in particularare more prone to be affected by cognitive retrogenesis, which caracterizes AD dementia.

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Speech rhythm alterations in Spanish-speaking individuals with Alzheimer’s disease

Cited by 26 publications

References 67 publications

Speech Analysis by Natural Language Processing Techniques: A Possible Tool for Very Early Detection of Cognitive Decline?

Speech Analysis by Natural Language Processing Techniques: A Possible Tool for Very Early Detection of Cognitive Decline?

Acoustic Feature Extraction with Interpretable Deep Neural Network for Neurodegenerative Related Disorder Classification

La habilidad léxico-semántica en la Enfermedad de Alzheimer: Un estudio de la fluidez verbal con categorías semánticas

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