Recognizing spoken Urdu numbers using Fourier descriptor and neural networks with Matlab

In this paper frequency analysis of spoken Urdu numbers from 'sifr' (zero) to 'nau' (nine) is described. Sound samples from multiple speakers were utilized to extract different features. Initial processing of data, i.e. time-slicing and normalizing and was done using a combination of Simulink and MATLAB. Afterwards, the same tools were used for calculation of Fourier descriptions and correlations. The correlation allowed comparison of the same words spoken by the same and different speakers. The analysis presented in this paper is seen as the first step in creating an Urdu speech recognition system. The speech recognition feed-forward neural network models in Matlab were developed. The models and algorithm exhibited high training and testing accuracies. Our major work involves in future use of TI6000 DSK series or linear predictive coding. Such a system can be potentially utilized in implementation of a voice-driven help setup in different systems.