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.
Full Title of Study: “Advanced Voice Analysis With Machine Learning Algorithms in Patients With Neurologic Diseases”
- Study Type: Observational
- Study Design
- Time Perspective: Prospective
- Study Primary Completion Date: July 31, 2022
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.
- Other: Speech task
- Speech task which consists of a sustained emission of the vowel /e/.
Arms, Groups and Cohorts
- Patients affected by neurologic disorders showing a prominent voice impairment.
Clinical Trial Outcome Measures
- 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
Participating in This Clinical Trial
- Clinical diagnosis of neurologic disorders Exclusion Criteria:
- smoking – bilateral/unilateral hearing loss – respiratory disorders – conditions affecting the vocal cords, including nodules.
Gender Eligibility: All
Minimum Age: N/A
Maximum Age: N/A
Are Healthy Volunteers Accepted: Accepts Healthy Volunteers
- Lead Sponsor
- Neuromed IRCCS
- Provider of Information About this Clinical Study
- Principal Investigator: Antonio Suppa, Principal Investigator – Neuromed IRCCS
- Overall Contact(s)
- Antonio Suppa, MD, PhD, 3494940365, firstname.lastname@example.org
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