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Detection of Common Cold from Speech Signals using Deep Neural Network
Suman Deb1, Pankaj Warule1, Amrita Nair1
1Sardar Vallabhbhai National Institute of Technology, Surat, 395007 India.
Circuits, Systems, and Signal Processing
|October 10, 2022
Summary
This study introduces a deep learning model to detect common colds from speech. The model accurately classifies cold speech using Mel-frequency cepstral coefficients and linear predictive coding, offering a noninvasive diagnostic tool.
Area of Science:
- Computational linguistics
- Speech processing
- Machine learning for healthcare
Background:
- The common cold, a viral respiratory illness, affects speech production by altering the vocal tract.
- Existing methods for cold detection are often invasive or lack real-time capabilities.
Purpose of the Study:
- To develop and evaluate a deep learning model for classifying speech affected by the common cold.
- To assess the model's performance against traditional machine learning classifiers.
Main Methods:
- Speech signals were analyzed using Mel-frequency cepstral coefficients (MFCC) and linear predictive coding (LPC).
- A deep learning model was trained on these features, with data imbalance addressed using SMOTE-Tomek links.
- Model performance was compared against logistic regression, random forest, and gradient boosted tree classifiers.
Main Results:
- The deep learning model achieved a higher Unweighted Average Recall (UAR) than the benchmark OpenSMILE SVM.
- The proposed method demonstrated comparable results to state-of-the-art techniques with reduced complexity and feature set size.
Conclusions:
- A novel, noninvasive deep learning approach for detecting the common cold via speech analysis has been successfully developed.
- This method holds potential for extension to the detection of other speech-affecting pathologies.
