A Novel Approach For Identifying Speech In Computational Intelligence
Abstract
The article examines the development of technology that recognizes speech, which is an especially significant use of artificial intelligence. Because AI is frequently featured in science fiction films, where people communicate with devices just like they'd interact with ordinary people, the concept is widely known. The two primary ways that humans naturally relate to their fellow humans are speech and actions. Speaking to a computer is made feasible by speech recognition. The equipment that converts spoken words into typographic text and directional instructions is called speech recognition programming. Speech recognition is utilized in legal and medical transcribing as well as in broadcasting news broadcasts along with live sports programming to create subtitles. It is challenging for technology to work out the spaces between sounds because there do not exist any pauses in spontaneously spoken English. The technique by which a computer converts an auditory voice signal to text is called automatic speech recognition.
By approximately 2010, these fundamental behavioral patterns will force the next level of technological developments into mainstream culture.
References
2. Shaikh Naziya S., Deshmukh R.R. (2016) speech Identifying system- a review. IOSR J. C
3. G. E. Dahl, M. Ranzato, A. Mohamed, and G. E. Hinton, “phone Identifying with the mean- Covariance restricted Boltzmann machine,” Adv. Neural Inf. Process. Syst., no. 23, 2010.
4. A. Mohamed, T. Sainath, G. Dahl, B. Ramabhadran, G. Hinton, and M. Pichney, “Completely belief networks using discriminative features for phone Identifying,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal process. (ICASSP), May 2011, pp. 5060-5063.
5. A. Mohamed, G. Dahl,and G. Hinton, “completely belief networks for phone identifying,” in proc. NIPS Workshop complete Learn. Speech Identify Related Applicat., 2009.
6. G. Dahl, D. Yu, L. Deng,and A. Acero, “Context-dependent pretained Completely artificial brain for large vocabulary speech identifying,” IEEE Trans. Audio, Speech, Lang. Process., vol. 20, no. 1, pp. 30-42, jan. 2012.
7. Nanni L., Costa Y. M., Aguiar R. L., Mangolin, R. B., Brahnam S., Silla C.N. (2020) Ensemble of Standard neural networks to improve animal audio classification. EURASIP journal on Audio, Speech, and Music Processing, 1-14.
8. Patel S. (2020) A Comprehensive Analysis of Standard Neural Network Models. International Journal of Advanced Science and Technology, 29(4), 771-777.
9. Kubanek M., Bobulski J., Kulawik, J.(2019) A method of speech coding for speech Identifying using a Standard neural network. Symmetry, 11(9), 1185.
10. Nwankpa C., Ijomah W., Gachagan, A., Marshall, S. (2018) Activation functions: Comparison of trends in practice and research for Complete learning. arXiv:1811.03378.
11. L. Deng, O. Abdel-Hamid, and D. Yu, “A deep standard neural network using heterogenous grouping for trading acoustic invariance with phonetic confusion,” in proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), May 2013, pp. 6669-6673.
12. T. N. Sainath, A-R. Mohamed, B. Kingsbury, and B. Ramabhadran, “Completely artificial brain for LVCSR,” in proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), May 2013, pp.8614-8618.
13. Poudel S. Anuradha, R. (2020) speech Identifying using Artificial neural networks.
14. Yang X., Yu H., Jia L. (2020) speech recognition of command words based on Identfyingl neural network.
