Performance of speaker recognition system using shifted mfcc, delta spectral cepstral coefficient (DSCC) and Fuzzy techniques

Abstract

Speech and speaker recognition systems are biometric inspired systems which are having scope in various online and offline applications. In case of biometric we ponder the variability of speech signal due to the presence of noise which greatly degrades the efficiency of Automatic Speaker Recognition (ASR) in real-world environmental circumstances. Real world speech signal is degraded by different types of noise signals like background noise, interference noise and crosstalk noise. In this paper, we have used Delta Spectrum Cepstrum Coefficient (DSCC) and Shifted MFCC with fuzzy modeling techniques to rectify the deed of ASR even in a noisy surrounding with the help of upgraded speech information which is present at high frequency in the spectral domain. The combination of fuzzy modeling and DSCC creates a firm cumulative algorithm which has reasonably high robustness to noise. Experimental results show that accuracy has enhanced by 10-20% even at 5-8dB SNR in the presence of background noise or turbulent environmental condition or in the presence of white noise. Thus proposed model has improved maturity level in comparison to obsolete methods.

Phoneme analysis for multiple languages with fuzzy‐based speaker identification

“…However, other methods such as vector quantisation [17,18] have their spots. A little research is made for fuzzy classification [8,[19][20][21][22], but most are quite dubious when describing their methods for both models and data. Furthermore, most recent research has converged to Neural Network variants, such as Deep Neural Networks [11,23,24], Convolutional Neural Networks [25], and others [26,27].…”

Investigating the use of multiple languages for crisp and fuzzy speaker identification

“…However, other methods such as vector quantisation (clustering) [15,16] have their spots. Little research is made for fuzzy classification [17,18,8,19,20], but the majority is quite dubious when describing their methods for both models and data. Furthermore, most recent research has converged to Neural Network variants, such as Deep Neural Networks [21,12,5], Convolutional Neural Networks [22], and others [23,24].…”