Researchers have developed a novel AI platform employing convolutional neural networks and Mel-spectrogram analysis to objectively stage unilateral vocal cord paralysis (UVCP). The model classifies voice recordings into severity categories—decompensated, partially compensated, or fully compensated—across a cohort of 423 subjects. This technology reduces diagnostic subjectivity inherent to laryngoscopic evaluations, potentially expediting treatment planning. By analyzing deep audio features, the TripleConvNet architecture represents a state-of-the-art advance linking AI and otolaryngology diagnostics.