Unveiling optimal mother wavelets by COPRAS Method Analyzing speech signals despite face mask and shield obstacles

Area of Science:

• Signal Processing
• Acoustics
• Biomedical Engineering

Background:

• Speech intelligibility is significantly reduced by face masks and shields, a problem exacerbated during the COVID-19 pandemic.
• Wavelet Transform (WT) is a powerful time-frequency analysis tool, but its effectiveness hinges on appropriate mother wavelet selection.
• Existing methods lack a standardized approach for selecting mother wavelets for speech signals under varying acoustic conditions.

Purpose of the Study:

• To propose and validate a novel methodology for selecting the optimal mother wavelet for speech signal analysis.
• To address the challenges in speech signal processing caused by face coverings.
• To establish a comprehensive protocol for mother wavelet selection in diverse speech signal scenarios.

Main Methods:

• Speech signals were collected under various conditions, including the use of different face masks and shields.
• The COPRAS (Complex Proportional Assessment) technique was employed to rank potential mother wavelet functions.
• Maximum Cross-Correlation Coefficient (MCC) and Maximum Energy to Shannon Ratio (MEER) were used as evaluation criteria for ranking.

Main Results:

• The proposed COPRAS-based methodology successfully identified optimal mother wavelets for speech signals processed with face coverings.
• The evaluation criteria (MCC and MEER) effectively differentiated the performance of various mother wavelets.
• A clear protocol was established, demonstrating improved accuracy in speech signal analysis.

Conclusions:

• The developed protocol provides a robust framework for selecting appropriate mother wavelets in wavelet transform applications for speech signals.
• This research offers a significant advancement in accurately analyzing speech under real-world conditions, such as those involving face masks.
• The findings contribute to enhancing speech processing technologies, particularly in communication and assistive devices.