Comparison of MFCC and DWT features for automatic speech recognition of Urdu

Mel Frequency Cepstral Coefficients (MFCCs) features have been the strongest candidate for work on automatic speech recognition. An alternative to MFCCs can be the use of features based on Discrete Wavelet Transform. This paper compares the performance of an automatic speech recognition framework based on MFCCs and DWT features. The framework uses Urdu isolated words corpus and the training and test data remain the same for both types of features. The classification has been achieved using Linear Discriminant Analysis.