Wavelet analysis is a prominent time-frequency analysis method in investigating various signals such as speech, vibration, acoustic signals, ultrasound, and underwater acoustic signals. Throughout the coronavirus pandemic, people have adopted diverse face shields and face masks, which have caused difficulties in understanding speech. To address this issue, the wavelet transform (WT), a proven effective method, can be implemented. Time-frequency analysis serves as a standard approach since it combines useful information between time-domain observations and frequency-domain data. However, the selection of an appropriate mother wavelet represents the main obstacle when using WT. The same signal produces different outcomes when analyzed with various mother wavelet selections. In this research, speech signals were obtained under various conditions of face masks and face shields. This work proposes the COPRAS (COmplex PRoportional ASsessment) technique to select the appropriate mother wavelet function. Maximum Cross-Correlation Coefficient (MCC) and Maximum Energy to Shannon Ratio (MEER) evaluation criteria are utilized to rank the better mother wavelet function. From the results, the proposed methodology establishes a comprehensive protocol for selecting mother wavelet for the speech signal in various conditions.