Advertisement
Published in:
2015 | OriginalPaper | Chapter
Sparsity Analysis and Compensation for i-Vector Based Speaker Verification
Authors : Wei Li, Tian Fan Fu, Jie Zhu, Ning Chen
Published in: Speech and Computer
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
Over recent years, i-vector based framework has been proven to provide state-of-art performance in speaker verification. Most of the researches focus on compensating the channel variability of i-vector. In this paper we will give an analysis that in the case that the duration of enrollment or test utterance is limited, i-vector based system may suffer from biased estimation problem. In order to solve this problem, we propose an improved i-vector extraction algorithm which we term Adapted First order Baum-Welch Statistics Analysis (AFSA). This new algorithm suppresses and compensates the deviation of first order Baum-Welch statistics caused by phonetic sparsity and phonetic imbalance. Experiments were performed based on NIST 2008 SRE data sets, Experimental results show that 10 %–15 % relative improvement is achieved compared to the baseline of traditional i-vector based system.