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Neuro-Fuzzy Logic Application in Speech Recognition

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Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science (ICMMCS 2023)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1450))

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Abstract

Speech recognition is the ability of a system to recognise words and phrases in speech and convert them to readable or text format. In general, speech recognition will be accomplished by activities such as call routing, speech-to-text processing, voice dialling, voice audibility, and language modelling. Although neural networks are good classifiers, their effectiveness is based on the calibre and quantity of training data they are given. The use of fuzzy approaches enhances performance when training data is lacking or not entirely representative of the possible range of values. In other words, adding fuzzy approaches enables the classification of erroneous data. This paper presents a neural network that handles fuzzy numbers as the neuro-fuzzy system. This characteristic gives the system the ability to classify erroneous input in the proper way. The performance of the neuro-fuzzy system for speaker-independent voice recognition is significantly better than a regular neural network, according to experimental findings. Due to variances in voice frequency and pronunciation, speaker-independent speech recognition is a particularly challenging categorisation challenge in this study.

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References

  1. Somarathi, S., & Vamshi, S. (2013). Design of NEURO fuzzy systems. International Journal of Information and Computation Technology, 3(8), 819–824.

    Google Scholar 

  2. Guz, Y. K., & Guney, I. (2010). Adaptive neuro-fuzzy inference system to improve the power quality of variable-speed wind power generation system. Turkish Journal of Electrical Engineering & Computer Sciences, 18(4), 625–645.

    Google Scholar 

  3. Kumari, N., Sunita, S. (2013). Comparision of ANNs, fuzzy logic and neuro-fuzzy integrated approach for diagnosis of coronary heart disease: A survey. International Journal of Computer Science and Mobile Computing, 2(6), 216–224.

    Google Scholar 

  4. Balbinot, A., & Favieiro, G. (2013). A neuro-fuzzy system for characterization of arm movements. Sensors, 13, 2613–2630.

    Article  Google Scholar 

  5. Vaidhehi, V. (2014). A framework to design a web based neuro fuzzy system for course advisor. International Journal of Innovative Research in Advanced Engineering, 1(1), 186–190.

    Google Scholar 

  6. Petchiathan, G., Valarmathi, K., Devaraj, D., & Radhakrishnan, T. K. (2014). Local linear model tree and neuro-fuzzy system for modelling and control of an experimental pH neutralization process. Brazilian Journal of Chemical Engineering, 31(2), 483–495.

    Article  Google Scholar 

  7. Ramesh, K., Kesarkar, A. P., Bhate, J., Ratnam, M. V., Jayaraman, A. (2015). Adaptive neuro-fuzzy inference system for temperature and humidity profile retrieval from microwave radiometer observations. Atmosphere Measurement Techniques, 8, 369–384.

    Google Scholar 

  8. Dragomir, O. E., Dragomir, F., Stefan, V., Minca, E. (2015) Adaptive neuro-fuzzy inference systems as a strategy for predicting and controlling the energy produced from renewable sources. Energies, 8, 13047–13061.

    Google Scholar 

  9. Junior, C. A. A., Silva, L. F. D., Silva, M. L. D., Leite, H. G., Valdetaro, E. B., Donato, D. B., & Castro, R. V. O. (2016). Modelling and forecast of charcoal prices using a neuro-fuzzy system. Cerne, 22(2), 151–158.

    Google Scholar 

  10. Chauduri, N. B., Chandrika, D., Kumari, D. K. (2016) A review on mental health using soft computing and neuro-fuzzy techniques. International Journal of Engineering Trends and Technology, 390–394.

    Google Scholar 

  11. Maskara, S., Kushwaha, A., Bhardwaj, S. (2016). Adaptive neuro-fuzzy system for cancer. International Journal of Innovative Research in Computer and Communication Engineering, 4(6), 11944–11948.

    Google Scholar 

  12. Markopoulos, A. P., Georgiopoulos, S., Kinigalakis, M., & Manolakos, D. E. (2016). Adaptive neuro-fuzzy inference system for end milling. Journal of Engineering Science and Technology, 11(6), 1234–1248.

    Google Scholar 

  13. Shaabani, M. E., Banirostam, T., & Hedayati, A. (2016). Implementation of neuro fuzzy system for diagnosis of multiple sclerosis. International Journal of Computer Science and Network, 5(1), 157–164.

    Google Scholar 

  14. Mathur, N., Glesk, I., & Buis, A. (2016). Comparision of adaptive neuro-fuzzy inference system (ANFIS) and Gaussian processes for machine learning (GPML) algorithms for the prediction of skin temperature in lower limb prostheses. Medical Engineering and Physics, 38(2016), 1083–1089.

    Article  Google Scholar 

  15. Hernandez, U. M., Solis, A. R., Panoutsos, G., Sanij, A. D. (2017). A combined adaptive neuro-fuzzy and Bayesian for recognition and prediction of gait events using wearable sensors. IEEE International Conference on Fuzzy Systems, 34–34.

    Google Scholar 

  16. Sahin, M., & Erol, I. R. (2017). A comparative study of neural networks and ANFIS for forecasting attendance rate of soccer games. Mathematical and Computer Applications, 22(43), 1–12.

    Google Scholar 

  17. Mamak, M., Unes, F., Kaya, Z. Y., Demirci, M. (2017). Evaporation prediction using adaptive neuro-fuzzy inference system and Penman FAO. In “Environmental Engineering” 10th International conference vilnius gediminas technical university (pp. 1–5).

    Google Scholar 

  18. Hadroug, N., Hafaifa, A., Guemana, M., Kouzou, A., Salam, A., & Chaibet, A. (2017). Heavy duty gas turbine monitoring based on adaptive neuro-fuzzy inference system: Speed and exhaust temperature control. Mathematics-in-Industry Case Studies, 8(8), 1–20.

    Google Scholar 

  19. Pradeep, M., Padmaja, V., & Himabindu, E. (2018). Adaptive neuro-fuzzy based UPQC in a distributed power system for enhancement of power quality. Helix, 8(2), 3170–3175.

    Article  Google Scholar 

  20. Atsalakis, G. S. (2018). Applications of a neuro-fuzzy system for welders’ indisposition forecasting. Journal of Scientific and Engineering Research, 5(4), 171–182.

    Google Scholar 

  21. An, V. G., Anh, T. T., Bao, P. T. (2018). Using genetic algorithm combining adaptive neuro-fuzzy inference system and fuzzy differential to optimizing gene. MOJ Proteomics Bioinformatics, 7(1), 65–72

    Google Scholar 

  22. Wending, L. (2022). Implementing the hybrid neuro-fuzzy system to model specific learning disability in special University education programs. Journal of Mathematics, 2022:6540542

    Google Scholar 

  23. Vani, H., Anusuya, M. (2020). Fuzzy speech recognition: a review. International Journal of Computer Applications, 177(47), 39–54

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics, Rajalakshmi Institute of Technology, Chennai, India

    D. Nagarajan

  2. Department of Mathematics, Vels Institute of Science, Technology and Advanced Studies, Chennai, India

    Khusbhu Chourashia

  3. Vels Institute of Science, Technology and Advanced Studies, Chennai, India

    A. Udhayakumar

Authors
  1. D. Nagarajan
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  2. Khusbhu Chourashia
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  3. A. Udhayakumar
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Corresponding author

Correspondence to D. Nagarajan .

Editor information

Editors and Affiliations

  1. Department Of Creative Technologies and Product Design, National Taipei University of Business, Taoyuan, Taiwan

    Sheng-Lung Peng

  2. School of Computer Science, SCS, Taylor's University, Subang Jaya, Malaysia

    Noor Zaman Jhanjhi

  3. Department of Computer Science and Engineering, Sister Nivedita University, Kolkata, West Bengal, India

    Souvik Pal

  4. College of Engineering and Computer Science, Joshi Research Center 489, Wright State University, Dayton, OH, USA

    Fathi Amsaad

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Nagarajan, D., Chourashia, K., Udhayakumar, A. (2023). Neuro-Fuzzy Logic Application in Speech Recognition. In: Peng, SL., Jhanjhi, N.Z., Pal, S., Amsaad, F. (eds) Proceedings of 3rd International Conference on Mathematical Modeling and Computational Science. ICMMCS 2023. Advances in Intelligent Systems and Computing, vol 1450. Springer, Singapore. https://doi.org/10.1007/978-981-99-3611-3_1

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