Speech-related parameters are sensitive measures of acetylcholinesterase inhibitor therapy in mild Alzheimer's disease.

Our aim was to find out whether speech-related temporal parameters (SRTPs) are sensitive indicators of the clinical outcome in acetylcholinesterase (AChE) inhibitor therapy with donepezil, compared to the standard cognitive Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) used in clinical trials. In this 24-week-long, naturalistic, self-control, open-labeled, prospective pilot study with 10 mg donepezil on 20 mild AD patients, cognitive functions were evaluated using 15 different SRTPs analyzed by automatic speech recognition in the Speech-Gap Test® compared to ADAS-Cog test results. Among the SRTPs, the filled pause duration ratio significantly improved after 12 weeks of donepezil treatment.

Extracting Phonetic Posterior-Based Features for Detecting Multiple Sclerosis From Speech.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system which, in addition to affecting motor and cognitive functions, may also lead to specific changes in the speech of patients. Speech production, comprehension, repetition and naming tasks, as well as structural and content changes in narratives, might indicate a limitation of executive functions. In this study we present a speech-based machine learning technique to distinguish speakers with relapsing-remitting subtype MS and healthy controls (HC).

Using Hybrid HMM/DNN Embedding Extractor Models in Computational Paralinguistic Tasks.

The field of computational paralinguistics emerged from automatic speech processing, and it covers a wide range of tasks involving different phenomena present in human speech. It focuses on the non-verbal content of human speech, including tasks such as spoken emotion recognition, conflict intensity estimation and sleepiness detection from speech, showing straightforward application possibilities for remote monitoring with acoustic sensors. The two main technical issues present in computational paralinguistics are (1) handling varying-length utterances with traditional classifiers and (2) training models on relatively small corpora.

Narrative recall in relapsing-remitting multiple sclerosis: A potentially useful speech task for detecting subtle cognitive changes.

Our research studied relapsing-remitting multiple sclerosis (RRMS). In half of the RRMS cases, mild cognitive difficulties are present, but often remain undetected despite their adverse effects on individuals' daily life. Detecting subtle cognitive alterations using speech analysis have rarely been implemented in MS research.

Optimizing the Ultrasound Tongue Image Representation for Residual Network-Based Articulatory-to-Acoustic Mapping.

Within speech processing, articulatory-to-acoustic mapping (AAM) methods can apply ultrasound tongue imaging (UTI) as an input. (Micro)convex transducers are mostly used, which provide a wedge-shape visual image. However, this process is optimized for the visual inspection of the human eye, and the signal is often post-processed by the equipment.

Temporal Speech Parameters Detect Mild Cognitive Impairment in Different Languages: Validation and Comparison of the Speech-GAP Test® in English and Hungarian.

Background: The development of automatic speech recognition (ASR) technology allows the analysis of temporal (time-based) speech parameters characteristic of mild cognitive impairment (MCI). However, no information has been available on whether the analysis of spontaneous speech can be used with the same efficiency in different language environments.

Objective: The main goal of this international pilot study is to address the question of whether the Speech-Gap Test® (S-GAP Test®), previously tested in the Hungarian language, is appropriate for and applicable to the recognition of MCI in other languages such as English.

Temporal Speech Parameters Indicate Early Cognitive Decline in Elderly Patients With Type 2 Diabetes Mellitus.

Introduction: The earliest signs of cognitive decline include deficits in temporal (time-based) speech characteristics. Type 2 diabetes mellitus (T2DM) patients are more prone to mild cognitive impairment (MCI). The aim of this study was to compare the temporal speech characteristics of elderly (above 50 y) T2DM patients with age-matched nondiabetic subjects.

The Role of Silence in Verbal Fluency Tasks - A New Approach for the Detection of Mild Cognitive Impairment.

Objective: Most recordings of verbal fluency tasks include substantial amounts of task-irrelevant content that could provide clinically valuable information for the detection of mild cognitive impairment (MCI). We developed a method for the analysis of verbal fluency, focusing not on the task-relevant words but on the silent segments, the hesitations, and the irrelevant utterances found in the voice recordings.

Methods: Phonemic ('k', 't', 'a') and semantic (animals, food items, actions) verbal fluency data were collected from healthy control (HC; = 25; = 67.

Telltale silence: temporal speech parameters discriminate between prodromal dementia and mild Alzheimer's disease.

This study presents a novel approach for the early detection of mild cognitive impairment (MCI) and mild Alzheimer's disease (mAD) in the elderly. Participants were 25 elderly controls (C), 25 clinically diagnosed MCI and 25 mAD patients, included after a clinical diagnosis validated by CT or MRI and cognitive tests. Our linguistic protocol involved three connected speech tasks that stimulate different memory systems, which were recorded, then analyzed linguistically by using the PRAAT software.

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

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.