Comparison of MPEG-7 basis projection features and MFCC applied to robust speaker recognition

Our purpose is to evaluate the efficiency of MPEG-7 basis projection (BP) features vs. Mel-scale Frequency Cepstrum Coefficients (MFCC) for speaker recognition in noisy environments. The MPEG-7 feature extraction mainly consists of a Normalized Audio Spectrum Envelope (NASE), a basis decomposition algorithm and a spectrum basis projection. Prior to the feature extraction the noise reduction algorithm is performed by using a modified log spectral amplitude speech estimator (LSA) and a minima controlled noise estimation (MC). The noise-reduced features can be effectively used in a HMM-based recognition system. The performance is measured by the segmental signalto-noise ratio, and the recognition results of the MPEG-7 standardized features vs. Mel-scale Frequency Cepstrum Coefficients (MFCC) in comparison to other noise reduction methods. Results show that the MFCC features yield better performance compared to MPEG-7 features.