Phonetic temporal neural model for language identification

Tang, Zhiyuan and Wang, Dong and Chen, Yixiang and Li, Lantian and Abel, Andrew (2018) Phonetic temporal neural model for language identification. IEEE/ACM Transactions on Audio Speech and Language Processing, 26 (1). pp. 134-144. ISSN 2329-9304 (https://doi.org/10.1109/TASLP.2017.2764271)

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Abstract

Deep neural models, particularly the long short-term memory recurrent neural network (LSTM-RNN) model, have shown great potential for language identification (LID). However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminativeDNNas the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: It is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions.

  • Item type:Article
    ID code:86660
    Dates:
    Date
    Event
    31 January 2018
    Published
    18 October 2017
    Published Online
    8 October 2017
    Accepted
    Notes:© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:01 Sep 2023 14:15
    Last modified:01 May 2024 01:27
    URI:https://strathprints.strath.ac.uk/id/eprint/86660
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