Voice Analysis in Patients With Neurologic Diseases

March 21, 2023 updated by: Antonio Suppa, Neuromed IRCCS

Advanced Voice Analysis With Machine Learning Algorithms in Patients With Neurologic Diseases

In this observational pilot study, the investigators will record and assess voice samples from healthy participants and those participants affected by neurologic diseases to evaluate possible differences in voice features.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

In this study, the investigators will evaluate the clinical features of healthy participants and those participants with neurologic disorders by applying dedicated clinical scales. Also, the investigators will assess voice impairment by using perceptual examination tools. Then, the investigators will apply spectral analysis to assess the main frequency components of voice in healthy participants and in patients affected by neurologic disorders with a prominent voice impairment. To distinguish between healthy participants and patients affected by various neurologic diseases, the investigators will apply a voice analysis based on support vector machine (SVM) classifier that included a large number of features in addition to the main frequency components of voice.

For these purposes, the investigators will assess in detail the sensitivity, specificity, positive predictive value, and negative predictive value and accuracy of all diagnostic tests. Furthermore, the investigators will calculate the area under the receiver operating characteristic (ROC) curves to verify the optimal diagnostic threshold as reflected by the associated criterion (Ass. Crit.) and Youden Index (YI). To assess possible clinical-instrumental correlations, the investigators will also use a modified algorithm of SVM analysis to calculate a continuous numerical value (the likelihood ratio [LR]) providing a measure of voice impairment severity for each participant.

Voice recordings will be performed by asking participants to produce a specific speech task with their usual voice intensity, pitch, and quality. The speech task will consist of a sustained emission of a close mid-front unrounded vowel /e/ for at least 5 seconds. Voice recordings will be collected by using a high-definition audio-recorder placed at a distance of 5 cm from the mouth. Voice samples will be recorded in linear PCM format (.wav) at a sampling rate of 44.1 kHz, with 24-bit sample size. Voice analysis will consist of three separate processes: feature extraction, selection and classification. For feature extraction, the investigators will use the OpenSMILE (audEERING GmbH, Germany), dedicated software. Then, the investigators will select and classify voice feature by using SVM algorithm included in Weka.

Study Type

Observational

Enrollment (Anticipated)

100

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

We will recruit neurologic patients not taking any oral medications or alcohol and any drugs acting on the central nervous system at the time of the study.

Description

Inclusion Criteria:

  • Clinical diagnosis of neurologic disorders

Exclusion Criteria:

  • smoking
  • bilateral/unilateral hearing loss
  • respiratory disorders
  • conditions affecting the vocal cords, including nodules.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients
Patients affected by neurologic disorders showing a prominent voice impairment.
Speech task which consists of a sustained emission of the vowel /e/.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Voice analysis
Time Frame: Voice analysis with machine learning algorithms will be implemented immediately after voice recording, during the clinical evaluation of each participant.
Voice features obtained by using Support Vector Machine algorithm
Voice analysis with machine learning algorithms will be implemented immediately after voice recording, during the clinical evaluation of each participant.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 1, 2021

Primary Completion (Actual)

July 31, 2022

Study Completion (Anticipated)

July 31, 2023

Study Registration Dates

First Submitted

April 12, 2021

First Submitted That Met QC Criteria

April 12, 2021

First Posted (Actual)

April 15, 2021

Study Record Updates

Last Update Posted (Actual)

March 22, 2023

Last Update Submitted That Met QC Criteria

March 21, 2023

Last Verified

March 1, 2023

More Information

Terms related to this study

Other Study ID Numbers

  • DIPNEUROSCI_01

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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