Performance Analysis of Thewarped Discrete Cosine Transform Cepstrum with MFCC Using Different Classifiers

In this paper, we continue our investigation of the warped discrete cosine transform cepstrum (WDCTC), which was earlier introduced as a new speech processing feature (Muralishankar et al., 2005). Here, we study the statistical properties of the WDCTC and compare them with the mel-frequency cepstral coefficients (MFCC). We report some interesting properties of the WDCTC when compared to the MFCC: its statistical distribution is more Gaussian-like with lower variance, it obtains better vowel cluster separability, it forms tighter vowel clusters and generates better codebooks. Further, we employ the WDCTC and MFCC features in a 5-vowel recognition task using vector quantization (VQ), 1-nearest neighbour (1-NN), probabilistic neural network (PNN) and Gaussian discriminant analysis (GDA) as classifiers. Finally, we discuss the vowel recognition results in the context of the statistical properties of the WDCTC and MFCC. In our experiments, the WDCTC consistently outperforms the MFCC