Insights in-to-End Learning Scheme for Language Identification
Weicheng Cai, Zexin Cai, Wenbo Liu, Xiaoqi Wang, Ming Li
Abstract
A novel interpretable end-to-end learning scheme for language identification is proposed. It is in line with the classical GMM i-vector methods both theoretically and practically. In the end-to-end pipeline, a general encoding layer is employed on top of the frontend CNN, so that it can encode the variable-length input sequence into an utterance level vector automatically. After comparing with the state-of-the-art GMM i-vector methods, we give insights into CNN, and reveal its role and effect in the whole pipeline. We further introduce a general encoding layer, illustrating the reason why they might be appropriate for language identification. We elaborate on several typical encoding layers, including a temporal average pooling layer, a recurrent encoding layer and a novel learnable dictionary encoding layer. We conducted experiment on NIST LRE07 closed-set task, and the results show that our proposed end-to-end systems achieve state-of-the-art performance.
BibTeX
@inproceedings{icassp2018_insightsintoendl,
title = {Insights in-to-End Learning Scheme for Language Identification},
author = {Weicheng Cai and Zexin Cai and Wenbo Liu and Xiaoqi Wang and Ming Li},
booktitle = {ICASSP 2018},
year = {2018}
}