ASR Features Extraction Using MFCC And LPC: A Comparative Study
Bassel Alkhatib, Mohammad Madian Kamal Eddin
Journal of Digital Information Management, Vol. 21, No. 2, 2023.
DOI: https://doi.org/10.6025/jdim/2023/21/2/39-49
Link: https://www.dline.info/fpaper/jdim/v21i2/jdimv21i2_2.pdf
Abstract:
The field of Automatic Speaker Recognition (ASR) is an important and open field for researchers and scientists, especially as it has become essential in facilitating the work we do in our daily lives. Such as digital authentication and electronic transactions, and consider It as a secure environment to authenticate users' access to their accounts. Many technologies have been developed in the field of recognition but so far, there is no complete tool or method for speaker identification, the most important step in ASR is the extraction of voice features. Many methods and tools can be used to extract the speaker's vocal characteristics (voice features), which in turn will identify the user and recognize his voice spectrum through the phonetic linguistic message. In this paper, two methods will be studied, each using a different technique MFCC, which uses a logarithmic scale, and LPC, which uses a linear scale. The method used in ASR should have minimal error because it is an important authentication technology like a fingerprint, where two different people cannot have the same voice spectral range (voiceprint).