Abstract
Speech recognition can be an important tool in today’s society for hand-free or voice-driven implementation. Using simple commands or triggers, it is possible for speech impaired human beings to communicate with increased ease of understanding. With the advent of various soft computing methods, a large class of nonlinearities can be handled. Artificial Neural Networks (ANN) have been applied in finding the solution for speech recognition. A lot of work is going on in this regard and mostly positive results have been achieved. Now, the research is being done to minimize the rate of error in obtaining the solution. In this paper, a comprehensive study of use of artificial neural networks in speech recognition is studied and proposes methods for training of the neural network so that an appropriate neural output can be obtained which is as close to the desired output. The paper demonstrates that ANN can indeed form the basis for a general-purpose speech recognition and neural network offers clear advantages over conventional methods. MATLAB simulation has been carried out to validate the results.
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References
Kamble, B.C.: Speech recognition using artificial neural network - a review. Int. J. Comput. Commun. Instrum. Eng. (2016)
Tebelskis, J.: Speech recognition using Neural Networks. Doctoral Dissertation, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania (1995)
Gupta, A., Joshi, A.: Speech recognition using artificial neural network. In: International Conference on Communication and Signal Processing (ICCSP), Chennai, pp. 0068–0071 (2018)
Murat, G.D., Sazli, H.: Speech recognition with artificial neural networks. Digit. Signal Process. 20(3), 763–768 (2010)
Alhawiti, K. M.: Advances in artificial intelligence using speech recognition. Int. J. Comput. Inf. Eng. (2015)
Singh, M., Sreejeth, M., Hussain, S.: Implementation of Levenberg-Marquadrt algorithm for control of induction motor drive. In: 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, pp. 865–869 (2018)
Bose, B. K.: Modern Power Electronics and AC Drives. Prentice Hall (2002)
Al Smadi, K., Al Issa, H., Trrad, I., Al Smadi, T.: Artificial intelligence for speech recognition based on neural networks. J. Signal Inf. Process. (2015)
Smys, S., Chen, J.I.Z., Shakya, S.: Survey on neural network architectures with deep learning. J. Soft Comput. Paradig. (JSCP) 2(03), 186–194 (2020)
Gupta, A., Joshi, A.: Speech recognition using artificial neural network. In: 2018 International Conference on Communication and Signal Processing (ICCSP), Chennai, pp. 0068–0071 (2018). https://doi.org/10.1109/ICCSP.2018.8524333
Graves, A., Jaitly, N., Mohamed, A.: Hybrid speech recognition with deep bidirectional LSTM. ASRU, pp. 273–278 (2013)
Hraskoa, R., Pacheco, A.G.C., Krohlinga, R.A.: Time series prediction using restricted Boltzmann machines and backpropagation
Generating sequences with recurrent neural networks, June 2014 (2014)
Buragohain, M.: Adaptive Network based Fuzzy Inference System (ANFIS) as a tool for system identification with special emphasis on training data minimization. (Doctoral Dissertation, Indian Institute of Technology Guwahati, India (2018)
Hussain, S., Bazaz, M.A.: ANFIS implementation on a three phase vector controlled induction motor with efficiency optimisation. In: International Conference on Circuits, Systems, Communication and Information Technology Applications (CSCITA) 04/2014, pp. 391–396 (2014)
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Hussain, S., Nazir, R., Javeed, U., Khan, S., Sofi, R. (2022). Speech Recognition Using Artificial Neural Network. In: Raj, J.S., Palanisamy, R., Perikos, I., Shi, Y. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_7
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DOI: https://doi.org/10.1007/978-981-16-2422-3_7
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