Robust Speaker Recognition in Noisy Environments, 2014 SpringerBriefs in Speech Technology Series

This book discusses speaker recognition methods to deal with realistic variable noisy environments. The text covers authentication systems for; robust noisy background environments, functions in real time and incorporated in mobile devices. The book focuses on different approaches to enhance the accuracy of speaker recognition in presence of varying background environments. The authors examine: (a) Feature compensation using multiple background models, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models.

Robust Speaker Verification – A Review.- Speaker Verification in Noisy Environments using Gaussian Mixture Models.- Stochastic Feature Compensation for Robust Speaker Verification.- Robust Speaker Modeling for Speaker Verification in Noisy Environments.

K. Sreenivasa Rao, Associate Professor, School of Information Technology, Indian Institute of Technology Kharagpur (IIT Kharagpur). Sourjya Sarkar is a graduate student at the Indian Institute of Technology Kharagpur.

Discusses the effect of noise, stochastic feature compensation methods based on Gaussian Mixture models (GMMs) Demonstrates the standards for speaker databases and noisy environments Includes supplementary material: sn.pub/extras