Semnan University Press Journal of Modeling in Engineering 2008-4854 21 72 2023 03 21 Automatic Speaker Recognition based on Gabor Features and Convolutional Neural Networks Automatic Speaker Recognition based on Gabor Features and Convolutional Neural Networks 49 67 7302 10.22075/jme.2022.26690.2245 FA Abdolreza Rashno Department of Computer Engineering, Engineering Faculty, Lorestan University, Khorramabad, Iran Sadegh Fadaei Department of Electrical Engineering, Faculty of Engineering, Yasouj University, Yasouj, Iran Abdolsamad Hamidi Department of Electrical Engineering, Engineering Faculty, Lorestan University, Khorramabad, Iran Journal Article 2022 03 24 Human voice contains characteristics such as: ethnicity, gender, feelings, age and other information, and speaker recognition identifies people based on their voice. Although researchers have worked in this area over the years and provide methods to improve the speaker recognition accuracy, there are still challenges. In this paper, a new speaker recognition method is proposed based on Gabor filter bank and convolutional neural networks. At first, spectrogram of the speech signal is formed and then, effective Gabor filter bank is designed so that these filters are suitable for extracting effective features of the speech signal. In the next step, spectrogram of the signal is passed through the Gabor filter bank to extract the speech signal features. Finally, speaker recognition is done using a convolutional neural network. Two datasets Aurora2 and TIMIT are used to evaluate the proposed method. Results show that the accuracy of the proposed method is competitive with the state-of-the-art methods. Human voice contains characteristics such as: ethnicity, gender, feelings, age and other information, and speaker recognition identifies people based on their voice. Although researchers have worked in this area over the years and provide methods to improve the speaker recognition accuracy, there are still challenges. In this paper, a new speaker recognition method is proposed based on Gabor filter bank and convolutional neural networks. At first, spectrogram of the speech signal is formed and then, effective Gabor filter bank is designed so that these filters are suitable for extracting effective features of the speech signal. In the next step, spectrogram of the signal is passed through the Gabor filter bank to extract the speech signal features. Finally, speaker recognition is done using a convolutional neural network. Two datasets Aurora2 and TIMIT are used to evaluate the proposed method. Results show that the accuracy of the proposed method is competitive with the state-of-the-art methods. https://modelling.semnan.ac.ir/article_7302_c39012521bb41c7d63616dbfc6768d78.pdf