A Robust Isolated Automatic Speech Recognition System using Machine Learning Techniques
Sunanda Mendiratta1, Neelam Turk2, Dipali Bansal3

1Sunanda Mendiratta* , Department of Electronics Engineering, J. C. Bose UST, Faridabad, India.
2Neelam Turk, Department of Electronics Engineering, J. C. Bose UST, Faridabad, India.
3Dipali Bansal, ECE Department, FET, Manav Rachna International Institute of Research and Studies, Faridabad, India.

Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 2325-2331 | Volume-8 Issue-10, August 2019 | Retrieval Number: J87650881019/2019©BEIESP | DOI: 10.35940/ijitee.J8765.0881019
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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: In order to make fast communication between human and machine, speech recognition system are used. Number of speech recognition systems have been developed by various researchers. For example speech recognition, speaker verification and speaker recognition. The basic stages of speech recognition system are pre-processing, feature extraction and feature selection and classification. Numerous works have been done for improvement of all these stages to get accurate and better results. In this paper the main focus is given to addition of machine learning in speech recognition system. This paper covers architecture of ASR that helps in getting idea about basic stages of speech recognition system. Then focus is given to the use of machine learning in ASR. The work done by various researchers using Support vector machine and artificial neural network is also covered in a section of the paper. Along with this review is presented on work done using SVM, ELM, ANN, Naive Bayes and kNN classifier. The simulation results show that the best accuracy is achieved using ELM classifier. The last section of paper covers the results obtained by using proposed approaches in which SVM, ANN with Cuckoo search algorithm and ANN with back propagation classifier is used. The focus is also on the improvement of pre-processing and feature extraction processes.
Keywords: Speech recognition system, SVM, kNN, ANN, Cuckoo search optimization, ELM
Scope of the Article: Machine Learning