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Comparison of Deep Learning Methods for Spoken Language Identification

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12335)


In this paper, we implement and compare deep learning based spoken language identification models. We also deploy two very recent and popular speech recognition methods, namely Wav2Vec and SpecAugment, in our classifiers and test if they are also applicable to the field of language identification. Out of the models we implement, X-vector based deep feed forward network classifier obtains the highest F1-score of 0.91, where the target set consists of five languages. SpecAugment data augmentation method turns out to increase the classification accuracy when applied to the input mel-spectrograms of the CRNN architecture. Although they obtain lower classification accuracies than some of the other methods, Wav2Vec speech representations also achieve promising results.


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Correspondence to Ali Haznedaroglu .

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Korkut, C., Haznedaroglu, A., Arslan, L. (2020). Comparison of Deep Learning Methods for Spoken Language Identification. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2020. Lecture Notes in Computer Science(), vol 12335. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60275-8

  • Online ISBN: 978-3-030-60276-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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