On the Use of Distributed DCT in Speaker Identification

Feature extraction is one of the most significant stage in development of a speaker identification (SI) system. Most of the SI systems use mel-frequency cepstral coefficient (MFCC) as a parameter for representing the speech signal into compact form. MFCC are extracted through spectral weighting by a bank of overlapping triangular filters followed by a de-correlation process. Conventionally, discrete cosine transform (DCT-II) is used for de-correlation. In this paper, we propose the usage of a better de-correlation algorithm for MFCC. In traditional method DCT was applied coarsely to all the filterbank energies. In the proposed technique we have incorporated the DCT in a distributed manner. The experimental results on two publicly available database, each consisting more than 130 speakers show that the proposed method improves the performance over baseline MFCC based SI system for various number of filters in the filterbank.