Paper Info
Title
Novel Quality Metric for Duration Variability Compensation in Speaker Verification using i-Vectors
Abstract
Automatic speaker verification (ASV) is the process to recognize persons using voice as biometric. The ASV systems show considerable recognition performance with sufficient amount of speech from matched condition. One of the crucial challenges of ASV technology is to improve recognition performance with speech segments of short duration. In short duration condition, the model parameters are not properly estimated due to inadequate speech information, and this results poor recognition accuracy even with the state-of-the-art i-vector based ASV system. We hypothesize that considering the estimation quality during recognition process would help to improve the ASV performance. This can be incorporated as a quality measure during fusion of ASV systems. This paper investigates a new quality measure for i-vector representation of speech utterances computed directly from Baum-Welch statistics. The proposed metric is subsequently used as quality measure during fusion of ASV systems. In experiments with the NIST SRE 2008 corpus, We have shown that inclusion of proposed quality metric exhibits considerable improvement in speaker verification performance. The results also indicate the potentiality of the proposed method in real-world scenario with short test utterances.
Year
DOI
Venue
2017
10.1109/ICAPR.2017.8593127
2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR)
Keywords
DocType
Volume
Short-segments,Duration Variability,Baum-Welch Statistics,Quality Measure,GMM-UBM,i-vector,Fusion,Speaker Recognition
Conference
abs/1812.00828
ISBN
Citations 
PageRank 
978-1-5386-2242-1
0
0.34
References 
Authors
11
3
Name
Order
Citations
PageRank
Arnab Poddar162.13
Md. Sahidullah232624.99
Goutam Saha3112.21