Paper Info
Title
Comparison of I-vector and GMM-UBM approaches to speaker identification with TIMIT and NIST 2008 databases in challenging environments.
Abstract
In this paper, two models, the I-vector and the Gaussian Mixture Model-Universal Background Model (GMM-UBM), are compared for the speaker identification task. Four feature combinations of I-vectors with seven fusion techniques are considered: maximum, mean, weighted sum, cumulative, interleaving and concatenated for both two and four features. In addition, an Extreme Learning Machine (ELM) is exploited to identify speakers, and then Speaker Identification Accuracy (SIA) is calculated. Both systems are evaluated for 120 speakers from the TIMIT and NIST 2008 databases for clean speech. Furthermore, a comprehensive evaluation is made under Additive White Gaussian Noise (AWGN) conditions and with three types of Non Stationary Noise (NSN), both with and without handset effects for the TIMIT database. The results show that the I-vector approach is better than the GMM-UBM for both clean and AWGN conditions without a handset. However, the GMM-UBM had better accuracy for NSN types.
Year
Venue
Field
2017
European Signal Processing Conference
TIMIT,Mel-frequency cepstrum,Noise measurement,Extreme learning machine,Computer science,Artificial intelligence,Pattern recognition,Speech recognition,Gaussian,NIST,Additive white Gaussian noise,Interleaving,Database
DocType
ISSN
Citations 
Conference
2076-1465
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Musab T. S. Al-Kaltakchi192.80
W. L. Woo232549.88
S. S. Dlay319823.94
Jonathon A. Chambers400.34