This unified research describes spoken Urdu numbers investigative analysis from ‘siffar’ (zero) to ‘nau’ (nine) for making a concrete foundation in recognition of Urdu language. Sound samples from multiple speakers were utilized to extract different features using Fourier descriptor and Neural networks. Initial processing of data, i.e. normalizing and time-slicing was done using a combination of Simulink in MATLAB. Afterwards, the MATLAB tool box commands 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 laid a foundation step in exploring Urdu language in
developing an Urdu speech recognition system. In this paper the speech recognition feed-forward neural network models in Matlab were developed. The models and algorithm exhibited high training and testing accuracies. Our major goal work involves in the future use of TI
TMS320C6000 DSK series or linear predictive coding. Such a system can be potentially utilized in implementation of a voice-driven help setup in different systems. Such as multi media, voice controlled tele-customer services.