MFCC in audio signal processing for voice disorder: a review
Voice Disorder or Dysphonia has caught the attention of audio signal process engineers and researchers. The efficiency of several feature extraction and classifier implementation techniques in identifying voice abnormalities has been investigated. Mel-Frequency Cepstral Coefficient (MFCC) has been e...
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Springer
2025
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| Summary: | Voice Disorder or Dysphonia has caught the attention of audio signal process engineers and researchers. The efficiency of several feature extraction and classifier implementation techniques in identifying voice abnormalities has been investigated. Mel-Frequency Cepstral Coefficient (MFCC) has been extensively used as a feature extractor. This paper adopts a Comparative Review Method to assess the effectiveness of feature extraction and classifier methods in detecting voice disorders. By examining the pairing of the Mel-Frequency Cepstral Coefficient (MFCC) with various classifiers, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and other online or commercial classifiers, the study aims to review the robustness of MFCC in this context. The study also recognizes the significance of choosing the right database in light of the various aetiologies of pathological illnesses and its possible influence on the efficacy of voice disorder detection. ? The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. |
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