01591nas a2200157   4500008004100000245006200041210006200103260001200165300001200177490000700189520114100196100002101337700002101358700001701379856003701396       2022                        eng d00aDeep Learning Model CNN With LSTM For Speaker Recognition0 aDeep Learning Model CNN With LSTM For Speaker Recognition  c12/2022  a131-1470 v203 a<p><span style="color: rgb(51, 51, 51); font-family: Arial, Helvetica, sans-serif;">Speech recognition is one of the most important research fields nowadays because of its necessity in our daily lives and to raise the fields of security to the highest level, Itâ€™s a task of speech processing, and our main scope in this paper is on speaker verification, which is to identify persons from their voices where the process depends on digitizing the sound waves into a form that allows the system to deal with it. The verification process is based on the characteristics of the speaker's voice (voice biometrics) and sends it to a further process to extract the features of that voice using the feature extraction method and using AI techniques to perform the task of identification. MFCC is used for the task of features extraction and obtains the spectrogram of a given voice signal where it represents a bank of information about the voice and sends it to the CNN model for further processing for training the model on that signal to verify if the voice belongs to a user in the system or itâ€™s a new enrollment.</span></p>
1 aAlkhatib, Bassel1 aMadian, Mohammad1 aEddin, Kamal  uhttps://dline.info/newl/node/755