A Systematic Review of Expressive and Receptive Prosody in People With Dementia

The Relevance of Communication Between Alzheimer's Patients and Their Caregivers. Effective Prosody Strategies to Improve Communication

One of the most critical factors in Alzheimer's disease (AD) is communication between patients and caregivers. A relevant part of the way of speaking is what is known as prosody, or the variations a speaker makes when talking. To our knowledge, no research has analyzed the relevance of communication for caregivers when speaking with AD patients or what they consider the most effective strategies to communicate with them. Therefore, this pilot study aims are twofold: to know the relevance caregivers (professionals and family) give to communication with AD patients; and to determine what prosody strategies they consider most effective. Two hundred fifty-two caregivers of AD patients (professional and family) participated in two online surveys, answering different questions about the relevance of communication and the best prosody strategies. They also performed an auditory perceptual assessment. The results showed that caregivers give communication a significant role in the patient's treatment behavior. They consider Alzheimer's (AD) patients should be spoken to with authority but with affection and positiveness. The most valued prosodic strategies were marked intonation, speaking affectionately, emphasizing essential words, a medium/low pitch, and a slow speed. This study highlights the value of communication in interacting with AD patients to improve their cognitive and emotional responses.

Deep Learning of Speech Data for Early Detection of Alzheimer's Disease in the Elderly

BACKGROUND

Alzheimer's disease (AD) is the most common form of dementia, which makes the lives of patients and their families difficult for various reasons. Therefore, early detection of AD is crucial to alleviating the symptoms through medication and treatment.

OBJECTIVE

Given that AD strongly induces language disorders, this study aims to detect AD rapidly by analyzing the language characteristics.

MATERIALS AND METHODS

The mini-mental state examination for dementia screening (MMSE-DS), which is most commonly used in South Korean public health centers, is used to obtain negative answers based on the questionnaire. Among the acquired voices, significant questionnaires and answers are selected and converted into mel-frequency cepstral coefficient (MFCC)-based spectrogram images. After accumulating the significant answers, validated data augmentation was achieved using the Densenet121 model. Five deep learning models, Inception v3, VGG19, Xception, Resnet50, and Densenet121, were used to train and confirm the results.

RESULTS

Considering the amount of data, the results of the five-fold cross-validation are more significant than those of the hold-out method. Densenet121 exhibits a sensitivity of 0.9550, a specificity of 0.8333, and an accuracy of 0.9000 in a five-fold cross-validation to separate AD patients from the control group.

CONCLUSIONS

The potential for remote health care can be increased by simplifying the AD screening process. Furthermore, by facilitating remote health care, the proposed method can enhance the accessibility of AD screening and increase the rate of early AD detection.

Analysis of emotional prosody as a tool for differential diagnosis of cognitive impairments: a pilot research

Introduction

This pilot research was designed to investigate if prosodic features from running spontaneous speech could differentiate dementia of the Alzheimer's type (DAT), vascular dementia (VaD), mild cognitive impairment (MCI), and healthy cognition. The study included acoustic measurements of prosodic features (Study 1) and listeners' perception of emotional prosody differences (Study 2).

Methods

For Study 1, prerecorded speech samples describing the Cookie Theft picture from 10 individuals with DAT, 5 with VaD, 9 with MCI, and 10 neurologically healthy controls (NHC) were obtained from the DementiaBank. The descriptive narratives by each participant were separated into utterances. These utterances were measured on 22 acoustic features via the Praat software and analyzed statistically using the principal component analysis (PCA), regression, and Mahalanobis distance measures.

Results

The analyses on acoustic data revealed a set of five factors and four salient features (i.e., pitch, amplitude, rate, and syllable) that discriminate the four groups. For Study 2, a group of 28 listeners served as judges of emotions expressed by the speakers. After a set of training and practice sessions, they were instructed to indicate the emotions they heard. Regression measures were used to analyze the perceptual data. The perceptual data indicated that the factor underlying pitch measures had the greatest strength for the listeners to separate the groups.

Discussion

The present pilot work showed that using acoustic measures of prosodic features may be a functional method for differentiating among DAT, VaD, MCI, and NHC. Future studies with data collected under a controlled environment using better stimuli are warranted.