|
KMID : 1022420170090020085
|
|
Phonetics and Speech Sciences
2017 Volume.9 No. 2 p.85 ~ p.94
|
|
|
Short utterance speaker verification using PLDA model adaptation and data augmentation
|
|
Yoo, Sung-Wook
Kwon Oh-Wook
|
|
|
Abstract
|
|
|
|
Conventional speaker verification systems using time delay neural network, identity vector and probabilistic linear discriminant analysis (TDNN-Ivector-PLDA) are known to be very effective for verifying long-duration speech utterances. However, when test utterances are of short duration, duration mismatch between enrollment and test utterances significantly degrades the performance of TDNN-Ivector-PLDA systems. To compensate for the I-vector mismatch between long and short utterances, this paper proposes to use probabilistic linear discriminant analysis (PLDA) model adaptation with augmented data. A PLDA model is trained on vast amount of speech data, most of which have long duration. Then, the PLDA model is adapted with the I-vectors obtained from short-utterance data which are augmented by using vocal tract length perturbation (VTLP). In computer experiments using the NIST SRE 2008 database, the proposed method is shown to achieve significantly better performance than the conventional TDNN-Ivector-PLDA systems when there exists duration mismatch between enrollment and test utterances
|
|
|
KEYWORD
|
|
|
time delay neural network (TDNN), identity vector (I-vector), probabilistic linear discriminant analysis (PLDA), vocal tract length perturbation (VTLP)
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
|