@article {749, title = {ASR Features Extraction Using MFCC And LPC: A Comparative Study-}, journal = {Journal of Digital Information Management}, volume = {21}, year = {2023}, month = {06/2023}, pages = {39-49}, type = {Research}, chapter = {39}, 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{\textquoteright} 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{\textquoteright}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).

}, doi = {https://doi.org/10.6025/jdim/2023/21/2/39-49}, url = {http://www.dline.info/download.php?sn=3757}, author = {Bassel Alkhatib and Mohammad Madian Kamal Eddin} } @article {757, title = {ASR Features Extraction Using MFCC and LPC: A Comparative Study-}, journal = {Journal of Digital Information Management}, volume = {20}, year = {2022}, month = {09-2022}, pages = {79-89}, type = {Research}, chapter = {79}, 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. Many technologies have been developed in the recognition field. Such as digital authentication and electronic transactions; consider It a secure environment to authenticate user{\textquoteright}s access to their accounts. Still, so far, there is no complete tool or method for speaker identification, the most crucial step in ASR is the extraction of voice features. Many techniques and tools can extract the speaker{\textquoteright}s vocal characteristics (voice features), identifying the user and recognising his voice spectrum through the phonetic and 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 a minimal error because it is a crucial authentication technology like a fingerprint, where two different people cannot have the same voice spectral range (voiceprint).

}, doi = {https://doi.org/10.6025/jdim/2022/20/3/79-89}, url = {http://www.dline.info/download.php?sn=3566}, author = {Mohammad Madian Kamal Eddin and Bassel Alkhatib} }