AIMC Topic: Dysphonia

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Different Performances of Machine Learning Models to Classify Dysphonic and Non-Dysphonic Voices.

Journal of voice : official journal of the Voice Foundation May 1, 2025
OBJECTIVE: To analyze the performance of 10 different machine learning (ML) classifiers for discrimination between dysphonic and non-dysphonic voices, using a variance threshold as a method for the selection and reduction of acoustic measurements use...
Humans Machine Learning Female Reproducibility of Results Young Adult Adult Male Middle Aged Case-Control Studies Predictive Value of Tests Bayes Theorem Signal Processing, Computer-Assisted Acoustics Sound Spectrography Speech Acoustics Speech Production Measurement Voice Quality Dysphonia
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Detection of Neurogenic Voice Disorders Using the Fisher Vector Representation of Cepstral Features.

Journal of voice : official journal of the Voice Foundation May 1, 2025
Neurogenic voice disorders (NVDs) are caused by damage or malfunction of the central or peripheral nervous system that controls vocal fold movement. In this paper, we investigate the potential of the Fisher vector (FV) encoding in automatic detection...
Humans Databases, Factual Female Reproducibility of Results Aged Young Adult Adult Male Middle Aged Case-Control Studies Support Vector Machine Predictive Value of Tests Signal Processing, Computer-Assisted Acoustics Sound Spectrography Speech Acoustics Vocal Cord Paralysis Vocal Cords Voice Disorders Speech Production Measurement Phonation Voice Quality Dysphonia
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