Automatic Speaker Recognition using MFCC and Artificial Neural Network
Kharibam Jilenkumari Devi1, Ayekpam Alice Devi2, Khelchandra Thongam3
1Kharibam Jilenkumari Devi, Department of Electronics and Communication Engineering, National Institute of Technology, Imphal (Manipur), India.
2Ayekpam Alice Devi, Department of Computer Science and Engineering, National Institute of Technology, Imphal (Manipur), India.
3Dr. Khelchandra Thongam, Department of Computer Science and Engineering, National Institute of Technology, Imphal (Manipur), India.
Manuscript received on 22 November 2019 | Revised Manuscript received on 03 December 2019 | Manuscript Published on 14 December 2019 | PP: 39-42 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10101191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1010.1191S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Automatic speaker recognition is the process of identification of a person automatically from his/her voices. A robust feature extraction algorithm is required for effective and efficient classification. In this paper, a new method is proposed for identifying the speaker using an artificial neural network. Here mel- frequency cepstral coefficient(MFCC) is used as a feature extraction technique that provides useful features for the recognition process. Using these extracted features value, input samples are then created and finally, classification is performed using Multilayer Perceptron (MLP) which is trained by backpropagation. This proposed method gives an accuracy of 94.44%.
Keywords: Speech Signal, Mel-frequency Cepstral Coefficient, Feedforward Neural Network, Back-Propagation.
Scope of the Article: Artificial Life and Societies