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Speech is the natural and simplest way of communicat ion and Speech Recognition is a fascinating application of Dig ital Signal Processing which has many real-world applicat ions. 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 combin ing these two techniques.

Financial Transactions in ATM Machines using Speech Signals