Paper Info
Title
Distilling Knowledge Using Parallel Data for Far-field Speech Recognition.
Abstract
In order to improve the performance for far-field speech recognition, this paper proposes to distill knowledge from the close-talking model to the far-field model using parallel data. The close-talking model is called the teacher model. The far-field model is called the student model. The student model is trained to imitate the output distributions of the teacher model. This constraint can be realized by minimizing the Kullback-Leibler (KL) divergence between the output distribution of the student model and the teacher model. Experimental results on AMI corpus show that the best student model achieves up to 4.7% absolute word error rate (WER) reduction when compared with the conventionally-trained baseline models.
Year
Venue
Field
2018
arXiv: Computation and Language
Computer science,Word error rate,Near and far field,Speech recognition,Natural language processing,Artificial intelligence
DocType
Volume
Citations 
Journal
abs/1802.06941
0
PageRank 
References 
Authors
0.34
14
4
Name
Order
Citations
PageRank
Jiangyan Yi11917.99
Jianhua Tao2848138.00
Zhengqi Wen38624.41
Bin Liu452.45