Recognition of Nigerian Major Languages Using Neural Networks

Ibikunle Frank^1, and Katende James¹

¹Botswana Int’l University of Science and Technology, Botswana

Cite this paper:
Ibikunle Frank and Katende James. Recognition of Nigerian Major Languages Using Neural Networks. Journal of Computer Networks. 2013; 1(2):32-37. doi: 10.12691/jcn-1-2-3

Abstract

Speech Recognition is the technology by which sounds, words or phrases spoken by humans are converted into electrical signals and these signals are transformed into coding patterns to which meanings are assigned. It has two main types: discrete word and continuous speech recognition systems. Each type can be further sub-divided into two categories as Speaker Dependent and Speaker Independent recognition systems. Speaker dependent system operates only on the speech of a particular speaker for which the system is trained, while the Speaker Independent systems can be operated on the speech of any speaker. The speech recognition system proposed here digitizes the isolated words spoken by a speaker and performs Mel Frequency ceptral analysis and other signal processing techniques on the digitized data. The processed speech signal is then passed on to a pattern recognition which takes action based on the type of command pattern received. Artificial Neural Network (ANN) is used as speech recognition engine. Two different corpora were collected of audio recordings of Yoruba, Igbo and Hausa language speakers, in which subjects read aloud different words. One of the collected corpora contained data with background noise and the other without background noise. The results obtained from simulation can be generalized to cater for larger vocabularies and for continuous speech recognition.

Keywords:
speech recognition Artificial Neural Networks MFCC Yoruba Igbo and Hausa languages

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Figures

References:

[1]	Rudnicky, A., Lee K., and Hauptmann A., (1992):“Survey of current speech technology”. Communications of the ACM, 37(3): pp52-57.
[2]	Picheny M., (2002). Large vocabulary speech recognition, IEEE Computer, 35(4):42-50.
[3]	Reddy D.R., (1976). Speech Recognition by Machine: a Review. Proceeding of IEEE, 64(4):501-531.
[4]	Odetunji Ajadi Odelobi, “Recognition of Tones in Yoruba Speech: experiments with Artificial Neural Network, Studies in Computational Intelligence 83, 23-47 (2008).
[5]	Mohamad Adnan etal., “Speech Recognition using Artificial NeuralNetworks and Hidden Markov Models”.
[6]	Burr, D. (1988). “Experiments on Neural Net Recognition of Spoken and Written Text”, IEEE Trans. on Acoustics, Speech, and Signal Processing, 36, pp 1162-1168.
[7]	Franzini, M., Witbrock, M., and Lee K., “Speaker-Independent Recognition of Connected Utterances using Recurrent and Non-Recurrent Neural Networks”, In Proc. International Joint Conference on Neural Networks, 1989.
[8]	Rabiner, L, and Wilpon, J., (1979). “Considerations in applying clustering techniques to speaker-independent word recognition” Journal of Acoustic Society of America, pp.663-673.
[9]	Haykin S, "Neural Networks: a comprehensive foundation", 2nd Edition, Prentice Hall, 1999.
[10]	LawenceRabinar& Bine-hwangJuang, “Fundamentals of speech recognition’. Prentice Hall, Englewood Cliffs, 1999.
[11]	Jurafsky, Daniel and Martin, James H, “Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition” (1st ed.). Prentice Hall, 1996.
[12]	Daoudi, K. (2002): “Automatic Speech Recognition: The New Millennium”, Proceedings of the 15th International Conference on Industrial and Engineering, Applications of Artificial Intelligence and Expert Systems: Developments in Applied Artificial Intelligence, pp 253-263.