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End-to-end DNN-CNN Classification for Language Identification

Jin, Ma, Song, Yan, McLoughlin, Ian Vince (2017) End-to-end DNN-CNN Classification for Language Identification. In: Proceedings of The World Congress on Engineering 2017. 1. pp. 119-203. IAENG ISBN 978-988-14-0474-9. (KAR id:61426)

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A defining problem in spoken language identification (LID) is how to design effective representations which allow features to be extracted that are specific to language information.

In this paper, a novel network is proposed and explored that models an effective representation using first and second-order statistics of features extracted from a well-trained phoneme-related DNN bottleneck network followed by a stack of CNN convolutional layers.

Evaluation with NIST LRE 2009 shows improved performance compared to current state-of-the-art systems, achieving over 33% and 20% relative equal error rate (EER) improvement for 3s and 10s utterances.

Item Type: Conference or workshop item (Proceeding)
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Ian McLoughlin
Date Deposited: 21 Apr 2017 09:28 UTC
Last Modified: 16 Feb 2021 13:44 UTC
Resource URI: (The current URI for this page, for reference purposes)
McLoughlin, Ian Vince:
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