Voice Enabled ATMs and Deployment of Wavelets for Recognition and Authentication of Voice Based Inputs

Indraneel Sreeram*, Venkata Praveen Kumar Vuppala**
* Professor, Department of Computer Science Engineering, St. Ann's College of Engineering & Technology, Chirala, A.P., India.
** Assistant Professor, Department of Computer Science Engineering, St. Ann's College of Engineering & Technology, Chirala, A.P., India.
Periodicity:December - February'2017
DOI : https://doi.org/10.26634/jcom.4.4.13417

Abstract

Automated Teller Machines (ATMs) enable customers to perform financial transactions like cash withdrawal, check balances or credit mobile phones with the help of the machine and without any human intervention. In this paper, a speech recognition system is developed for ATMs for performing financial transactions. Since speech is the most natural and easiest mode of communication, communication between the customer and the ATM can be performed easily through speech. This allows ATM machines to communicate with the customers using the stored speech samples and the user communicates with the machine through spoken digits. Here, two speech recognition systems are developed using the feature extraction techniques Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). The classifier used along with both the methods is Artificial Neural Network (ANN). Both methods produced good recognition accuracies of 87.575% and 85.5% each, but DWT and ANN combination produced better results than WPD and ANN.

Keywords

Automated Teller Machines, Feature Extraction, Discrete Wavelet Transforms, Wavelet Packet Decomposition, Pattern Recognition, Artificial Neural Networks

How to Cite this Article?

Sreeram,I., and Vuppala,V.P.K. (2017). Voice Enabled ATMS and Deployment of Wavelets for Recognition and Authentication of Voice Based Inputs. i-manager’s Journal on Computer Science, 4(4), 25-31. https://doi.org/10.26634/jcom.4.4.13417

References

[1]. Lawrence R. Rabiner, and Ronald W. Schafer, (2007). “Introduction to Digital Speech Processing”. Foundations and Trends in Signal Processing, Vol.1, No.1-2, pp.1-194.
[2]. Samudravijaya K., (2003). “Speech and Speaker recognition: A tutorial”. In Proc. International Workshop on Technology Development in Indian Languages, Kolkata.
[3]. Kuldeep Kumar, and R.K. Aggarwal, (2011). “Hindi Speech Recognition System using HTK”. International Journal of Computing and Business Research. Vol.2, No.2.
[4]. Neuro AI, (n.d.). Speech Recognition. Retrieved from: http://www.learnartificialneuralnetworks.com/speech recgnition.html
[5]. Benzeghiba, M., De Mori, R., Deroo, O., Dupont, S., Erbes, T., Jouvet, D., and Rose, R., (2007). “Automatic speech recognition and speech variability: A review”. Speech Communication, Vol.49, No.10, pp.763-786.
[6]. Picone J.W., (1993). “Signal Modelling Technique in Speech Recognition”. In Proc. of the IEEE, Vol.81, No.9, pp.1215-1247.
[7]. Stolcke A., Shriberg E., Ferrer L., Kajarekar S., Sonmez K., and Tur G., (2007). “Speech Recognition as Feature Extraction for Speaker Recognition”. In Signal Processing Applications for Public Security and Forensics, 2007. SAFE'07. IEEE Workshop on, IEEE, Washington D.C., USA, pp.1-5.
[8]. Hu Dingyin, Li Wei, and Chen Xi, (2011). “Feature Extraction of Motor Imagery EEG Signals based on Wavelet Packet Decomposition”. In Proc. of the 2011 IEEE International Conference on Complex Medical Engineering, Harbin Heilongjiang, pp.694-697.
[9]. S. Mallat, (1999). A Wavelet Tour of Signal Processing. Academic Press, San Diego.
[10]. Elif Derya Ubeyil, (2009). “Combined Neural Network Model employing Wavelet Coefficients for ECG Signals Classification”. Digital signal Processing, Vol.19, No.2, pp.297-308.
[11]. S. Chan Woo, C. Peng Lin, and R. Osman, (2001). “Development of a Speaker Recognition System using Wavelets and Artificial Neural Networks”. In Proc. of 2001 Int. Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, pp.413-416.
[12]. S. Kadambe, and P. Srinivasan, (1994). “Application of Adaptive Wavelets for Speech”. Optical Engineering, Vol.33, No.7, pp.2204-2211.
[13]. S.G. Mallat, (1989). “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.11, No.7, pp.674-693.
[14]. Wavelet Packet Decomposition, (n.d.). In Wikipedia. Retrieved from: http://en.wikipedia.org/wiki/Wavelet_ packet_decomposition
[15]. Y. Hao, and X. Zhu, (2000). “A New Feature in Speech Recognition based on Wavelet Transform”. In Proc. of. IEEE th 5 Inter. Conf. on Signal Processing, Vol.3, pp.1526- 1529.
[16]. Freeman J.A., and Skapura D.M., (2006). Neural Networks, Algorithm, Application and Programming Techniques. Pearson Education.
[17]. Economou K., and Lymberopoulos D., (1999). “A New Perspective in Learning Pattern Generation for Teaching Neural Networks”. Vol.12, No.4-5, pp.767-775.
[18]. Anil K. Jain, Robert P.W. Duin, and Jianchang Mao, (2000). “Statistical Pattern Recognition: A Review”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.1, pp.4-37.
[19]. Eiji Mizutani, and James W. Demmel, (2003). “On Structure-exploiting Trust Region Regularized Nonlinear Least Squares Algorithms for Neural-Network Learning”. Neural Networks, Vol.16, No.5-6, pp.745-753.
[20]. Sonia Sunny, David Peter S., and K. Poulose Jacob, (2012). “Optimal Daubechies Wavelets for Recognizing Isolated Spoken Words with Artificial Neural Networks Classifier”. International Journal of Wisdom Based Computing, Vol.2, No.1, pp.35-41.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.