To this end, a large corpus of vowels from 45 speakers of Athenian Greek and Cypriot Greek was collected. This study provides a classification model of two Modern Greek dialects, namely Athenian Greek and Cypriot Greek, using information from speech. Dialect classification using vowel acoustic parameters. The Deep Neural Network Architecture proposed here constitutes a method that contributes to the early diagnosis of cognitive decline, quantifies the progression of the condition, and enables suitable therapeutics.įrontiers in Neurology 2018 version: In this study, we provide an automated machine learning method, using Deep Neural Network Architectures, that aims to identify MCI. To this day, there is no cure for dementia but early-stage treatment can delay the progression of MCI thus, the development of valid tools for identifying early cognitive changes is of great importance. Identification of Mild Cognitive Impairment from Speech in Swedish using Deep Sequential Neural Networks. Themistocleous Charalambos, Eckerström Marie, and Dimitrios Kokkinakis (2018). Frontiers in Communication 2019 version:.In our future research, we will be exploring further this approach to identify medical conditions that influence speech production. This study shows that using machine learning and information from a speech segment, namely a single sonorant sound /m n l r/, it is possible to distinguish two dialects of Greek: Athenian Greek and Cypriot Greek. Listeners do not require long productions of speech to identify the accent of a speaker, often a single sound suffices. Dialect Classification from a Single Sonorant Sound Using Deep Neural Networks. Link to GitHub page with source code:.Journal of Alzheimer's Disease, version:.The end-to-end automated machine learning approach enables clinicians and researchers to provide an easy, quick, and inexpensive classification of patients with PPA. In this paper, we present a machine learning model based on deep neural networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically. Automatic subtyping of individuals with Primary Progressive Aphasia.
Themistocleous Charalambos, Bronte Ficek, Kimberly Webster, Dirk-Bart den Ouden, Argye E.
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