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Sensors
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Sensors in Health Disease Detection Based on Speech Signals
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Sensors in Health Disease Detection Based on Speech Signals

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: 20 July 2024 | Viewed by 1181

Special Issue Editors

Dr. Ganesh Naik

E-Mail Website
Guest Editor
College of Science and Engineering, Flinders University, Adelaide, Australia
Interests: blind source separation; independent component analysis; biomedical signal processing; human computer interaction; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Tony Jan

E-Mail Website
Guest Editor
School of Design, Torrens University, Sydney, NSW 2007, Australia
Interests: cybersecurity for Internet of Things (IoT) applications; IoT fog analytics for real-time ICT applications (augmented reality); smart cities and real-time digital infrastructures (smart traffics and digital twins)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advances in modern sensors and signal processing techniques in medicine have improved the accuracy and reliability of medical diagnoses. Today, biomedical signal analysis, especially speech signals, is becoming one of biology and medicine's most crucial disease detection and interpretation methods.

Speech signals can be a valuable and objective tool supporting the diagnosis of many neurodegenerative diseases. The basic feasibility of speech-based disease detection or symptom severity prediction can already be demonstrated for a broad spectrum of medical conditions ranging from acute to chronic respiratory diseases.

New real-time methods are needed to improve the medical prognosis of diseases and shorten the time required for diagnosis. Speech can now be used as a digital biomarker for disease detection, which has advantages including easier diagnosis access, symptom monitoring, cost savings, and higher diagnostic precision.

This Special Issue aims to present a complete range of proven and new methods that play a leading role in improving biomedical diagnostics using speech signals. In this Special Issue, we want to build a bridge between different scientific disciplines and offer highly innovative researchers in various fields a platform to exchange research in this exciting and emerging field: Sensors in Health Disease Detection Based on Speech Signals.

Dr. Ganesh Naik
Prof. Dr. Tony Jan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biomedical applications
  • deep learning
  • digital biomarker
  • healthcare
  • machine learning
  • speech technology
  • non-invasive sensors
  • articulation points
  • voice analysis
  • pathological speech conditions

Published Papers (1 paper)

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Research

14 pages, 1597 KiB  
Article
Machine Learning-Assisted Speech Analysis for Early Detection of Parkinson’s Disease: A Study on Speaker Diarization and Classification Techniques
by Michele Giuseppe Di Cesare, David Perpetuini, Daniela Cardone and Arcangelo Merla
Sensors 2024, 24(5), 1499; https://doi.org/10.3390/s24051499 - 26 Feb 2024
Cited by 1 | Viewed by 964
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal [...] Read more.
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. One of the notable non-motor symptoms of PD is the presence of vocal disorders, attributed to the underlying pathophysiological changes in the neural control of the laryngeal and vocal tract musculature. From this perspective, the integration of machine learning (ML) techniques in the analysis of speech signals has significantly contributed to the detection and diagnosis of PD. Particularly, MEL Frequency Cepstral Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GTCCs) are both feature extraction techniques commonly used in the field of speech and audio signal processing that could exhibit great potential for vocal disorder identification. This study presents a novel approach to the early detection of PD through ML applied to speech analysis, leveraging both MFCCs and GTCCs. The recordings contained in the Mobile Device Voice Recordings at King’s College London (MDVR-KCL) dataset were used. These recordings were collected from healthy individuals and PD patients while they read a passage and during a spontaneous conversation on the phone. Particularly, the speech data regarding the spontaneous dialogue task were processed through speaker diarization, a technique that partitions an audio stream into homogeneous segments according to speaker identity. The ML applied to MFCCS and GTCCs allowed us to classify PD patients with a test accuracy of 92.3%. This research further demonstrates the potential to employ mobile phones as a non-invasive, cost-effective tool for the early detection of PD, significantly improving patient prognosis and quality of life. Full article
(This article belongs to the Special Issue Sensors in Health Disease Detection Based on Speech Signals)
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