Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Sri Krishna, A.
- Automatic Speech Recognition System using MFCC-based LPC approach with Back Propagated Artificial Neural Networks
Abstract Views :223 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science Engineering, Centurion University of Technology and Management, IN
2 Department of Information Technology, Shri Vishnu Engineering College for Women, IN
3 Department of Electronics and Communication Engineering, Centurion University of Technology and Management, IN
1 Department of Computer Science Engineering, Centurion University of Technology and Management, IN
2 Department of Information Technology, Shri Vishnu Engineering College for Women, IN
3 Department of Electronics and Communication Engineering, Centurion University of Technology and Management, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 4 (2020), Pagination: 2153-2159Abstract
Over the previous years, a marvelous quantity of study was performed by utilizing the artificial intelligence based deep learning approaches for the speech recognition applications. The automatic speech recognition (ASR) facing the problems in as preprocessing, feature extraction and classification stages mostly, thus solving these problems is mandatory to improve the classification accuracy of speech processing. To solve these issues, an advanced speech recognition methodology has developed by utilizing the Spectral Subtraction (SS) method of denoising with the combination of Mel-frequency Cepstral coefficients (MFCCs) and linear predictive coefficients (LPCs) feature extraction of speech signals. Then back propagated artificial neural networks (BP-ANN) is utilized for classifying the speech signals for the purpose of ASR, respectively. The simulation results show that the proposed approach gives the better classification accuracy compared to the state-of-ASR approaches..Keywords
Speech Processing, Automatic Speech Recognition, Mel-Frequency Cepstral Coefficients, Linear Predictive Coding, Artificial Neural Networks.
References
- H. Liu and H. Motoda, “Feature Selection for Knowledge Discovery and Data Mining”, Kluwer Academic Publishers, 2012.
- X. Tang, Y. Dai and Y. Xiang, “Feature Selection based on Feature Interactions with Application to Text Categorization”, Expert Systems with Applications, Vol. 120, pp. 207-216, 2019.
- K. Scarfone aand P. Mell, “Guide to Intrusion Detection and Prevention Systems (IDPS)”, Technical Report, National Institute of Standards and Technology, pp. 1-78, 2012.
- S. Mohammadi, H. Mirvaziri, M. Ghazizadeh Ahsaee and H. Karimipour, “Cyber Intrusion Detection by Combined Feature Selection Algorithm”, Journal of Information Security and Applications, Vol. 44, No. 2, pp. 80-88, 2019.
- S. Zaman and F. Karray, “Features selection for intrusion detection systems based on support vector machines”, Proceedings of 6th IEEE International Conference on Consumer Communications and Networking, pp. 1-8, 2009.
- S. Maza and M. Touahria, “Feature Selection Algorithms in Intrusion Detection System: A Survey”, KSII Transactions on Internet and Information Systems, Vol. 12, No. 10, pp. 1-14, 2018.
- K. Chen, F.Y. Zhou and X.F. Yuan, “Hybrid Particle Swarm Optimization with Spiral-Shaped Mechanism for Feature Selection”, Expert Systems with Applications, Vol. 128, pp. 140-156, 2019.
- M. Keshtgary and N. Rikhtegar, N., “Intrusion Detection Based on a Novel Hybrid Learning Approach”, Journal of AI and Data Mining, Vol. 6, No. 1, pp. 157-162, 2018.
- N. Acharya and S. Singh, “An IWD-Based Feature Selection Method for Intrusion Detection System.”, Soft Computing, Vol. 22, No. 13, pp. 407-416, 2018.
- A.S. Eesa, Z. Orman and A.M.A. Brifcani, “A New Feature Selection Model Based on ID3 and Bees Algorithm for Intrusion Detection System”, Turkish Journal of Electrical Engineering and Computer Sciences, Vol. 23, No. 2, pp. 615-622, 2015.
- E. Zorarpaci and S.A. Ozel, “A Hybrid Approach of Differential Evolution and Artificial Bee Colony for Feature Selection”, Expert Systems with Applications, Vol. 62, pp. 91-103, 2016.
- Barnali Sahu,Satchidananda Dehuri and Alok Jagadev, “A Study on the Relevance of Feature Selection Methods in Microarray Data”, The Open Bioinformatics Journal, Vol. 11, No. 2, pp. 117-139, 2018.
- Swagatam Das, Arijit Biswas, Sambarta Dasgupta and Ajith Abraham, “Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications”, Foundations of Computational Intelligence, Vol. 3, pp. 23-55, 2009.
- L.P. Dias and J.J.F. Cerqueira, “Using Artificial Neural Network in Intrusion Detection Systems to Computer Networks”, Proceedings of 9th IEEE International Conference on Computer Science and Electronic Engineering, pp. 1-8, 2017.
- J. Martens and I. Sutskever, “Learning Recurrent Neural Networks with Hessian-Free Optimization”, Proceedings of 28th IEEE International Conference on Machine Learning, pp. 1-6, 2011.
- Xuegong Zhang, Xin Lu and Qian Shi, “Recursive SVM Feature Selection and Sample Classification for Mass-Spectrometry and Microarray Data”, BMC Bioinformatics, Vol. 7, No. 19, pp. 1-18, 2006.
- V.R. Shewale and H.D. Patil, “Performance Evaluation of Attack Detection Algorithms using Improved Hybrid IDS with Online Captured Data”, International Journal of Computer Applications, Vol. 146, No. 8, pp. 1-12, 2016.
- S. Kalaivani and Gopinath Ganapath, “Bio-Inspired Modified Bees Colony Feature Selection based Intrusion Detection System for Cloud Computing Application”, International Journal of Advanced Science and Technology, Vol. 29, No. 3, pp. 1-12, 2020.
- S. Kalaivani and Gopinath Ganapath, “Bacterial Foraging Optimization for Enhancing the Security in Intrusion Detection System”, International Journal of Scientific and Technology Research, Vol. 9, No. 2, pp. 1-8, 2020.