Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system

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

CONSORT 2010 checklist of information to include when reporting a randomised trial*.

(DOC)</p

List of interventions.

(PDF)</p

Minimal underlying data.

(XLSX)</p

Map of Saudi Arabia with distribution of participating ICUs.

(PDF)</p

Distribution of compliance, non-compliance and contraindications (with reasons) to the interventions.

(PDF)</p

List of participating sites and ethics committee approvals.

(PDF)</p

Data measures definitions.

(PDF)</p

Characteristics of participating sites.

IQR: interquartile range. (PDF)</p

Change of daily process measures over the study period among the whole cohort and in subgroups of ICUs with baseline spontaneous trial (SAT) compliance of >50% and ≤50.

(PDF)</p