Sleep Deprivation (SD) is a growing issue that impairs cognitive, physical, and mental health and increases the risk of accidents and chronic diseases. Conventional detection methods are often intrusive, costly, or impractical for daily use. Aligned with United Nations Sustainable Development Goal 3 (UN SDG 3), this study aims to ensure healthy lives and promote well-being. The development of cost-effective, non-invasive, and accessible tools for SD detection is essential to integrate sleep health into public health approaches. The study developed a Support Vector Machine (SVM) trained by the researchers to classify their mild SD status through voice analysis. The researchers trained the model on an open-access dataset from the Open Science Framework (OSF), which was extracted through Spectro-Temporal Modulation (STM) features. To have a solution, the study evaluated the performance of SVM through STM features. It analyzed its performance across different dimensions of STM features, sessions, and Balanced Accuracy (BAcc) at the population and individual levels. The SVM achieved a training and testing BAcc of 0.8588 and 0.7476, respectively, which indicates sufficient performance and generalization. Statistical analyses are applied to determine the differences between the other trained models in different dimensions of STM: frequency-rate (FR), frequency-scale (FS), and scale-rate (SR). Analysis of Variance (ANOVA), Multivariate Analysis of Variance (MANOVA), and t-tests proved those hypotheses.