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

Volume 4 Issue 4 December - February 2017

Research Paper

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.

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

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.