Spectral Image-Based Diagnosis of Voice Disorders: Leveraging Spectrograms for Non-Invasive Assessment

This paper proposes a novel speech-based diagnosis method for laryngeal diseases without invasive procedures such as endoscopy. Existing methods have used sustained vowels and mfcc, melspectrogram to develop algorithms. However, these methods have limitations in terms of data availability and the complexity of the data. In this paper, we address these limitations by using connected speech and multi-patched spectrogram model. Connected speech is more complex than sustained vowels, and multi-patched spectrogram model can extract more information from the data. Experimental results show that the proposed method achieves an accuracy of over 91.5%, which is superior to existing methods. Also, we showed Grad-CAM visualization to highlight disease-related image regions, which could help clinicians to better understand the disease and develop more effective treatments.