Financial Transactions in ATM Machines using Speech Signals
Journal Title: International Journal of engineering Research and Applications - Year 2017, Vol 7, Issue 1
Abstract
Speech is the natural and simplest way of communication and Speech Recognition is a fascinating application of Digital Signal Processing which has many real-world applications. In this paper, a speech recognition system is developed for Automated Teller Machines (ATMs) using Wavelet Packet Decomposition (WPD) and Artificial Neural Networks (ANN). Speech signals are one-dimensional and are random in nature. ATM machines communicate with the customers using the stored speech samples and the user communicates with the machine using spoken digits. Daubechies wavelets are employed here. A multilayer neural network trained with back propagation training algorithm is used for classification purpose. The proposed method is implemented for 500 speakers uttering 10 spoken digits in English. The experimental results show good recognition accuracy of 87.38% and the efficiency of combining these two techniques.
Authors and Affiliations
Dr. Sonia Sunny
Keywords
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- EP ID EP389849
- DOI 10.9790/9622-0701042528.
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