Application Of Different Filters In Mel Frequency Cepstral Coefficients Feature Extraction And Fuzzy Vector Quantization Approach In Speaker Recognition

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 is strongly determined by the quality of the front-end. Over the years, Mel-Frequency 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 MelFrequency Cepstral Coefficients is one of the performance enhancement parameters for speaker recognition, which contains high frequency region complementary information in it and also introduces the Gaussian shaped filter (GF) and Tukey 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 and Tukey over traditional triangular filter (TF) based implementation, individually as well as in combination. In this study, Fuzzy Vector Quantization (FVQ) is used for speaker modeling. Fuzzy clustering methods allow objects to belong to several clusters simultaneously, with different degrees of membership. The performances of proposed GF and Tukey Filter based MFCC and IMFCC in individual and merged mode have been verified in two standard databases POLYCOST (Telephone Speech) and TIMIT each of which has more than 130 speakers as well as self voice collected from 90 speakers. 1.Introduction A speaker recognition system mainly consists of two main module, speaker specific feature extractor as a front end followed by a speaker modelling technique for generalized representation of extracted features [1, 2]. Since long time MFCC is considered as a reliable front end for a speaker recognition application because it has coefficients that represents audio, based on perception [3, 4]. In MFCC the frequency bands are positioned logarithmically which approximated the human auditory systems response more closely than the linear spaced frequency bands of FFT or DCT. An illustrative speaker recognition system is shown in figure.1. The state of the art speaker recognition research primarily investigates speaker specific complementary information relative to MFCC. It has been observed that the performance of speaker recognition improved significantly when complementary information is merged with MFCC in feature level either by simple concatenation or by combining models scores. The main complementary information is pitch [5], residual phase [6], prosody [7], dialectical features [8] etc. These features are related with vocal chord vibration and it is very difficult to extract speaker specific information. It has been shown that complementary information can be captured easily from the high frequency part of the energy spectrum of a speech frame via reversed filter bank methodology [9]. There are some features of speaker which used to present at high frequency part of the spectrum and generally ignored by MFCC that can be captured by inverted MFCC is proposed in this paper. The complementary information captured by inverted MFCC is modelled by FVQ [10] technique. In this paper, Fuzzy Vector Quantization (FVQ) is used for speaker modelling. In many real situations, fuzzy clustering is more natural than hard clustering, as objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial memberships.So the present study was undertaken with the objective of to find out the speaker recognition efficiency improving components. 3171 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 www.ijert. org Vol. 2 Issue 6, June 2013