Over the past few years, technology has become very dynamic. It is fuelling itself at an ever increasing rate. An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. Over the last decades many interesting techniques of Neural Network (NN) were introduced, and shown to be useful in many applications in different fields. Since neural network brings together techniques from different fields such as vowel recognition, pattern recognition, Character recognition, face recognition, pattern matching, image processing, signature verification, data compression, signal processing among many different sources. This paper presents a study survey of various method of vowel recognition. The methods included and analyzed in this survey are Knowledge Based Cascade Correlation (KBCC), Multilayer Perceptron, Formants, and Linear predictive features.

Vowel,speech,Speech recognition (SR), Knowledge Based Cascade Correlation (KBCC), Multilayer perceptron (MLP), linear predictive (LP)

[1] Franqois Rivest and Thomas R. Shultz “Application of Knowledge-based Cascade-correlation to Vowel Recognition”.
[2] Mihaela Grigore “Vowel recognition with non linear perceptron”.
[3] Hult, G. “Some vowel recognition experiments using multilayer perceptrons”
[4] Jeff Byorick, Ravi P. Ramachandran and Robi Polikar “Isolated Vowel Recognition Using Linear Predictive Features and Neural Network Classifier Fusion”
[5] Biljana Prica and Sini?sa Ili´c “Recognition of Vowels in Continuous Speech by UsingFormants”
[6] François Rivest and Thomas R. Shultz “Knowledge-based Cascade-correlation: A Review”
[7] Thomas R. Shultz and Francois Rivest “Knowledge-based Cascade-correlation: Varying the Size and Shape of Relevant Prior Knowledge”
[8] Hua Nong TING and Jasmy YUNUS “speaker independent malay vowel recognition of children using multi layer perceptron”
[9] Buckingham D, Shultz TR (2000) The developmental course of distance, time, and velocity concepts: A generative connectionist model. J Cog and Dev 1: 305–345
[10] Rivest F, Shultz TR (2002) Application of knowledge-based cascade-correlation to vowel recognition. IEEE Internat World Congr on Comp Intell, pp. 53–58
[11] Allison B (2007) The I of BCIs: next generation interfaces for brain–computer interface systems that adapt to individual users. In: International conference on human-computer interaction. Springer, Berlin, Heidelberg, pp 558–568
[12] Birbaumer N, Cohen LG (2007) Brain–computer interfaces: communication and restoration of movement in paralysis. J Physiol 579(3):621–636
[13] Faradji F, Ward RK, Birch GE (2009) Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis. J Neurosci Methods 180(2):330–339
[14] Nijboer F, Sellers EW, Mellinger J, Jordan MA, Matuz T, Furdea A, Halder S, Mochty U, Krusienski DJ, Vaughan TM, Wolpaw JR (2008) A P300-based brain–computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol 119(8):1909–1916
[15] Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ. EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng. 2018;15: 056013.
[16] DaSalla CS, Kambara H, Sato M, Koike Y. Spatial filtering and single-trial classification of EEG during vowel speech imagery. i-CREATe 2009—International Convention on Rehabilitation Engineering and Assistive Technology. Association for Computing Machinery; 2009. pp. 1–4. doi: 10.1145/1592700.1592731
[17] Tzovara A, Murray MM, Plomp G, Herzog MH, Michel CM, De Lucia M. Decoding stimulus-related information from single-trial EEG responses based on voltage topographies. Pattern Recognit. 2012;45: 2109–2122.
[18] Blankertz B, Lemm S, Treder M, Haufe S, Müller KR. Single-trial analysis and classification of ERP components—A tutorial. Neuroimage. 2011;56: 814–825.
[19] Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L. Large-scale video classification with convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014. pp. 1725–1732.
[20] An X, Kuang D, Guo X, Zhao Y, He L. A deep learning method for classification of EEG data based on motor imagery. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag; 2014. pp. 203–210.