Power-spectrum-based Mel-Frequency Cepstrum Coefficients (MFCC) is usually used as a feature extractor in a speaker identification system. This one-dimensional feature extraction subsystem, however, shows low recognition rates for identifying utterance speech signals under harsh noise conditions. In this paper, we have developed a speaker identification system based on Bispectrum data that is more robust to the addition of Gaussian noise. As one-dimensional MFCC method could not be directly used to process the two-dimensional Bispectrum data, we proposed a two-dimensional MFCC method and its optimization using Genetic Algorithm (GA). Experiments using the two-dimensional MFCC method as the feature extractor and a Hidden Markov Model as the pattern classifier on utterance speeches contained with various levels of Gaussian noise are conducted. Results showed that the developed system performed higher recognition rates compare with that of 1D-MFCC method, especially when the 2D-MFCC with GA optimization method is utilized.
|Number of pages||11|
|Journal||WSEAS Transactions on Computers|
|Publication status||Published - Aug 2012|
- 2D Mel-Frequency Cepstrum Coefficients
- Genetics Algorithms
- Hidden Markov Model
- Speaker Identification System