Title
End-to-end Language Identification using NetFV and NetVLAD.
Abstract
In this paper, we apply the NetFV and NetVLAD layers for the end-to-end language identification task. NetFV and NetVLAD layers are the differentiable implementations of the standard Fisher Vector and Vector of Locally Aggregated Descriptors (VLAD) methods, respectively. Both of them can encode a sequence of feature vectors into a fixed dimensional vector which is very important to process those variable-length utterances. We first present the relevances and differences between the classical i-vector and the aforementioned encoding schemes. Then, we construct a flexible end-to-end framework including a convolutional neural network (CNN) architecture and an encoding layer (NetFV or NetVLAD) for the language identification task. Experimental results on the NIST LRE 2007 close-set task show that the proposed system achieves significant EER reductions against the conventional i-vector baseline and the CNN temporal average pooling system, respectively.
Year
DOI
Venue
2018
10.1109/iscslp.2018.8706687
2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP)
Keywords
DocType
Volume
Task analysis,Encoding,Speech recognition,Acoustics,Standards,Probability,Recurrent neural networks
Conference
abs/1809.02906
Citations 
PageRank 
References 
1
0.37
16
Authors
6
Name
Order
Citations
PageRank
Jinkun Chen11648.45
weicheng cai2236.72
Danwei Cai3166.71
Zexin Cai422.75
Haibin Zhong510.71
Ming Li65595829.00