The deep neural network (DNN) was a remarkable modeling technology for speech recognition in recent years and its performance was significantly better than that of the Gaussian mixture model,which was the mainstream modeling technology in speech recognition before.However,commendable adaptation of DNN has not been solved yet.In this work,we use the identity vector (i-vector) to adapt a deep neural network by putting i-vector and the regular speech features together as the input of DNN for both training and testing.Then we focus on the normalization method of i-vector using a new max-min linear normalization method.We get a 5.10%relative decrease in word error rate over the traditional length normalization method.
YANG Jianbin
,
ZHANG Weiqiang
,
LIU Jia
. Investigation of normalization methods in speaker adaptation of deep neural network using i-vector[J]. Journal of University of Chinese Academy of Sciences, 2017
, 34(5)
: 633
-639
.
DOI: 10.7523/j.issn.2095-6134.2017.05.014
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