Deep Learning for Speech Recognition Systems: Evolution, Techniques, and Applications

Abstract

Deep learning has revolutionized the field of speech recognition by significantly
improving accuracy and scalability. This paper explores the role of deep learning architectures,
particularly deep neural networks (DNNs), in automatic speech recognition (ASR). A
comprehensive review of pre-2013 advancements highlights how these methods surpass
traditional approaches such as Gaussian Mixture Models (GMMs) and Hidden Markov Models
(HMMs). Using benchmark datasets, this study evaluates the performance of DNN-based
systems against conventional models. Results demonstrate the ability of deep learning to handle
diverse linguistic and acoustic variations effectively, paving the way for modern ASR
applications.

REFERENCES

1. Bergstra, J., & Bengio, Y. (2012). Random Search for Hyper-Parameter Optimization.
Journal of Machine Learning Research, 13, 281-305.
International Journal of Artificial Intelligence and Machine Learning in
Engineering 344|p
2. Caruana, R., & Gehrke, J. (2008). A Comparison of Decision Trees and Neural Networks for
Predictive Modeling. Machine Learning, 30(3), 223-236.
3. Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine.
Annals of Statistics, 29(5), 1189-1232.
4. Hutter, F., & Hoos, H. H. (2011). Automated Configuration of Machine Learning Algorithms.
In Proceedings of the 24th International Conference on Machine Learning (ICML), 1001-
1008.
5. Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News,
2(3), 18-22.
6. Sutton, R. S., & Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press.
7. Vanschoren, J. (2011). Meta-Learning: A Survey. Machine Learning, 84(1), 1-22.
8. Vlachos, M., & M. M. (2008). Data Mining: Methods and Techniques for Business
Intelligence. Springer.
9. Wang, Y., & Zhang, S. (2010). Automatic Machine Learning and Its Applications.
International Journal of Computational Intelligence and Applications, 9(4), 577-590.
10. Ravi Kumar Perumallapalli, Machine Learning Approaches for Improving Supply Chain
Efficiency and Demand Prediction - Perumallapalli Ravikumar - IJSAT Volume 1, Issue 2,
April-June 2010.
11. Ravi Kumar Perumallapalli, "AI-Driven Optimization of Healthcare Diagnostics: Early
Detection in Real-World Systems", IJCSPUB - INTERNATIONAL JOURNAL OF
CURRENT SCIENCE (www.IJCSPUB.org), ISSN:2250-1770, Vol.1, Issue 1, page no.76-86,
March 2011, Available :https://rjpn.org/IJCSPUB/papers/IJCSP11A1014.pdf
12. Ravi Kumar Perumallapalli, "Autonomous Vehicles: Real-Time AI for Safer Transportation
Networks", IJCSPUB - INTERNATIONAL JOURNAL OF CURRENT SCIENCE
(www.IJCSPUB.org), ISSN:2250-1770, Vol.1, Issue 2, page no.61-69, April 2011,
Available :https://rjpn.org/IJCSPUB/papers/IJCSP11B1012.pdf
13. Ravi Kumar Perumallapalli, " PREDICTIVE MAINTENANCE IN CLOUD
INFRASTRUCTURE: A MACHINE LEARNING FRAMEWORK", IJCSPUB -
INTERNATIONAL JOURNAL OF CURRENT SCIENCE (www.IJCSPUB.org),
ISSN:2250-1770, Vol.1, Issue 1, page no.106-115, January-2011,
Available :https://rjpn.org/IJCSPUB/papers/IJCSP11A1016.pdf
14. Ravi Kumar Perumallapalli, "AI-Enhanced Personalization in E-Commerce: Redefining
Customer Interaction", IJCSPUB - INTERNATIONAL JOURNAL OF CURRENT
SCIENCE (www.IJCSPUB.org), ISSN:2250-1770, Vol.2, Issue 1, page no.114-122, March-
2012, Available :https://rjpn.org/IJCSPUB/papers/IJCSP12A1017.pdf
15. Ravi Kumar Perumallapalli, "Machine Learning Algorithms for Accurate Stock Market
Forecasting: Case Studies 2012", IJCSPUB - INTERNATIONAL JOURNAL OF
CURRENT SCIENCE (www.IJCSPUB.org), ISSN:2250-1770, Vol.2, Issue 4, page no.57-64,
December-2012, Available :https://rjpn.org/IJCSPUB/papers/IJCSP12D1009.pdf
16. Ravi Kumar Perumallapalli, " NATURAL LANGUAGE PROCESSING FOR
AUTOMATED IT SERVICE DESK RESOLUTION", IJCSPUB - INTERNATIONAL
JOURNAL OF CURRENT SCIENCE (www.IJCSPUB.org), ISSN:2250-1770, Vol.2, Issue
1, page no.131-138, January-2012,
Available :https://rjpn.org/IJCSPUB/papers/IJCSP12A1019.pdf
17. Bengio, Y. (2009). Learning Deep Architectures for AI. Foundations and Trends® in
Machine Learning, 2(1), 1-127.
International Journal of Artificial Intelligence and Machine Learning in
Engineering 345|p
18. Bischl, B., & Kerschke, P. (2012). Benchmarking Machine Learning Algorithms: A Case
Study. Journal of Machine Learning Research, 14, 1-50.
19. Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-
297.
20. Hutter, F., & Hoos, H. H. (2009). Automated Configuration of Machine Learning Algorithms.
In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics
(AISTATS), 1-10.
21. Kohavi, R., & John, G. H. (1997). Wrappers for Feature Subset Selection. Artificial
Intelligence, 97(1-2), 273-324.
22. Olson, R. S., & Moore, J. H. (2010). Towards Automated Machine Learning: A Survey of
Frameworks. Proceedings of the International Conference on Data Mining, 34-42.
23. Rocci, A., & Valtorta, M. (2010). Auto-Weka: Automated Selection and Hyperparameter
Optimization for Weka. Journal of Machine Learning Research, 11, 1-50.
24. Sammut, C., & Webb, G. I. (2011). Encyclopedia of Machine Learning. Springer.
25. Wang, S., & Zeng, D. (2007). A Survey of Machine Learning Applications in Data Mining.
Data Mining and Knowledge Discovery, 14(2), 235-250.
26. Yang, Q., & Shen, X. (2010). Efficient Optimization for Large-Scale Machine Learning.
Machine Learning, 71(3), 1-28.
27. Zhang, S., & Zheng, L. (2009). Automated Machine Learning Algorithms and Applications.
In Proceedings of the International Conference on Computational Intelligence and Machine
Learning, 124-131.