<p>During the worldwide pandemic, people have been using various types of face masks and face shields to avoid infections. As a result, communication among humans has become more complex. In this work, we aimed to mitigate this complexity using our previously proposed method. Specifically, we focused on the Empirical Mode Decomposition (EMD) method to enhance acoustic signals and address the aforementioned challenge. This was done by utilizing intrinsic mode functions in the EMD method, which use Fast Fourier Transforms (FFTs) to differentiate between noise and signal. This identification allows for the mitigation of noise from useful content. Additionally, we considered the treatment of EMD to various acoustic signals by performing different experiments under various face mask conditions. We then compared the enhanced acoustic signals obtained from the EMD method with signals enhanced by other methods such as Spectral Subtraction (SS) and Wiener Filtering (WF)-based methods. The evaluation was performed using objective quality metrics including Segmental Signal-to-Noise Ratio (SSNR), Log-Spectral Distance (LSD), Short-Time Objective Intelligibility (STOI), and Perceptual Evaluation of Speech Quality (PESQ). The results from these experiments allowed us to conclude that the EMD speech enhanced acoustic signals are better than signals enhanced by any other method.</p>