Vector Quantization Approach for Speaker Recognition using MFCC and Inverted MFCC

Front-end or feature extractor is the first component in an automatic speaker recognition system. Feature extraction transforms the raw speech signal into a compact but effective representation that is more stable and discriminative than the original signal. Since the front-end is the first component in the chain, the quality of the later components (speaker modeling and pattern matching) is strongly determined by the quality of the front-end. In other words, classification can be at most as accurate as the features. Over the years, MelFrequency Cepstral Coefficients (MFCC) modeled on the human auditory system has been used as a standard acoustic feature set for speech related applications. In this paper it has been shown that the inverted Mel-Frequency Cepstral Coefficients is one of the performance enhancement parameters for speaker recognition, which contains high frequency region complementary information in it. This paper introduces the Gaussian shaped filter (GF) while calculation MFCC and inverted MFCC in place of traditional triangular shaped bins. The main idea is to introduce a higher amount of correlation between subband outputs. The performance of both MFCC and inverted MFCC improve with GF over traditional triangular filter (TF) based implementation, individually as well as in combination. In this study the Vector Quantization (VQ) feature matching technique was used, due to high accuracy and its simplicity. The proposed investigation achieved 98.57% of efficiency with a very short test voice sample 2 seconds.