Open AccessAdaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system
Author(s) -
Hesham Adnan Alabbasi,
Ali Muayad Jalil,
Fadhil Sahib Hasan
Publication year - 2020
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v10i5.pp5208-5216
Subject(s) - mel frequency cepstrum , computer science , speech recognition , robustness (evolution) , pattern recognition (psychology) , feature extraction , thresholding , artificial intelligence , wavelet , speaker recognition , speaker identification , mixture model , classifier (uml) , biochemistry , chemistry , image (mathematics) , gene
The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal Background Model Gaussian Mixture Model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios.