Result Details
A Noise Robust I-Vector Extractor Using Vector Taylor Series For Speaker Recognition
This article describes a successfull adapation of the VTS approach to speakerrecognition by proposing a new i-vector extraction framework.
speaker recognition, Vector Taylor Series, ivector,noisy speaker verification, noise compensation
We propose a novel approach for noise-robust speaker recognition, where the model of distortions caused by additive and convolutive noises is integrated into the i-vector extraction framework. The model is based on a vector taylor series (VTS) approximation widely successful in noise robust speech recognition. The model allows for extracting "cleaned-up" i-vectors which can be used in a standard i-vector back end. We evaluate the proposed framework on the PRISM corpus, a NIST-SRE like corpus, where noisy conditions were created by artificially adding babble noises to clean speech segments. Results show that using VTS i-vectors present significant improvements in all noisy conditions compared to a state-of-theart baseline speaker recognition. More importantly, the proposed framework is robust to noise, as improvements are maintained when the system is trained on clean data.
@inproceedings{BUT103500,
author="Yun {Lei} and Lukáš {Burget} and Nicolas {Scheffer}",
title="A Noise Robust I-Vector Extractor Using Vector Taylor Series For Speaker Recognition",
booktitle="Proceedings of ICASSP 2013",
year="2013",
pages="6788--6791",
publisher="IEEE Signal Processing Society",
address="Vancouver",
isbn="978-1-4799-0355-9",
url="http://www.fit.vutbr.cz/research/groups/speech/publi/2013/lei_icassp2013_0006788.pdf"
}IARPA Building Speech Recognition for Keyword Search in a New Language in a Week with Limited Training Data (BABEL) - Babelon, BBN, start: 2012-03-05, end: 2016-11-04, completed
Technologies of speech processing for efficient human-machine communication, TAČR, Program aplikovaného výzkumu a experimentálního vývoje ALFA, TA01011328, start: 2011-01-01, end: 2014-12-31, completed