Automatic Speaker Recognition based on Gabor Features and Convolutional Neural Networks
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 ar...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
| Published: |
Semnan University
2023-03-01
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| Series: | مجله مدل سازی در مهندسی |
| Subjects: | |
| Online Access: | https://modelling.semnan.ac.ir/article_7302_c39012521bb41c7d63616dbfc6768d78.pdf |
| Summary: | 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. |
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| ISSN: | 2008-4854 2783-2538 |