Spoken Language Identification Using ConvNets

  • Sarthak
  • Shikhar ShuklaEmail author
  • Govind Mittal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11912)


Language Identification (LI) is an important first step in several speech processing systems. With a growing number of voice-based assistants, speech LI has emerged as a widely researched field. To approach the problem of identifying languages, we can either adopt an implicit approach where only the speech for a language is present or an explicit one where text is available with its corresponding transcript. This paper focuses on an implicit approach due to the absence of transcriptive data. This paper benchmarks existing models and proposes a new attention based model for language identification which uses log-Mel spectrogram images as input. We also present the effectiveness of raw waveforms as features to neural network models for LI tasks. For training and evaluation of models, we classified six languages (English, French, German, Spanish, Russian and Italian) with an accuracy of 95.4% and four languages (English, French, German, Spanish) with an accuracy of 96.3% obtained from the VoxForge dataset. This approach can further be scaled to incorporate more languages.


Language Identification Raw waveform Convolutional Neural Networks Machine learning 


  1. 1.
    Bartz, C., Herold, T., Yang, H., Meinel, C.: Language identification using deep convolutional recurrent neural networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) Neural Information Processing. LNCS, vol. 10639, pp. 880–889. Springer, Cham (2017). Scholar
  2. 2.
    Bergstra, J., Yamins, D., Cox, D.D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures (2013)Google Scholar
  3. 3.
    Chen, L., et al.: Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5659–5667 (2017)Google Scholar
  4. 4.
    Dehak, N., Torres-Carrasquillo, P.A., Reynolds, D., Dehak, R.: Language recognition via i-vectors and dimensionality reduction. In: Twelfth Annual Conference of the International Speech Communication Association (2011)Google Scholar
  5. 5.
    Endah Safitri, N., Zahra, A., Adriani, M.: Spoken language identification with phonotactics methods on minangkabau, sundanese, and javanese languages. Procedia Comput. Sci. 81, 182–187 (2016). Scholar
  6. 6.
    Ferrer, L., Scheffer, N., Shriberg, E.: A comparison of approaches for modeling prosodic features in speaker recognition. In: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 4414–4417. IEEE (2010)Google Scholar
  7. 7.
    Ganapathy, S., Han, K., Thomas, S., Omar, M., Segbroeck, M.V., Narayanan, S.S.: Robust language identification using convolutional neural network features. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)Google Scholar
  8. 8.
    Gazeau, V., Varol, C.: Automatic spoken language recognition with neural networks. Int. J. Inf. Technol. Comput. Sci. (IJITCS) 10(8), 11–17 (2018)Google Scholar
  9. 9.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp. 770–778 (2016).
  10. 10.
    Hershey, S., et al.: CNN architectures for large-scale audio classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 131–135. IEEE (2017)Google Scholar
  11. 11.
    Howard, J., et al.: Fastai (2018).
  12. 12.
    Kumar, P., Biswas, A., Mishra, A.N., Chandra, M.: Spoken language identification using hybrid feature extraction methods. arXiv preprint arXiv:1003.5623 (2010)
  13. 13.
    Lee, J., Kim, T., Park, J., Nam, J.: Raw waveform-based audio classification using sample-level CNN architectures. arXiv preprint arXiv:1712.00866 (2017)
  14. 14.
    LibROSA: Accessed 16 July 2019
  15. 15.
    Lopez-Moreno, I., Gonzalez-Dominguez, J., Plchot, O., Martinez, D., Gonzalez-Rodriguez, J., Moreno, P.: Automatic language identification using deep neural networks. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5337–5341. IEEE (2014)Google Scholar
  16. 16.
    Martinez, D., Plchot, O., Burget, L., Glembek, O., Matějka, P.: Language recognition in ivectors space. In: Twelfth Annual Conference of the International Speech Communication Association (2011)Google Scholar
  17. 17.
    Montavon, G.: Deep learning for spoken language identification. In: NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, pp. 1–4 (2009)Google Scholar
  18. 18.
    Obuchi, Y., Sato, N.: Language identification using phonetic and prosodic HMMs with feature normalization. In: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2005), vol. 1, pp. I–569. IEEE (2005)Google Scholar
  19. 19.
    Revay, S., Teschke, M.: Multiclass language identification using deep learning on spectral images of audio signals. arXiv preprint arXiv:1905.04348 (2019)
  20. 20.
    Tong, R., Ma, B., Zhu, D., Li, H.,Chng, E.S.: Integrating acoustic, prosodic and phonotactic features for spoken language identification. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 1, p. I, May 2006.
  21. 21.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  22. 22.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)Google Scholar
  23. 23. Free speech recognition (Linux, Windows and mac) - Accessed 16 July 2019
  24. 24.
    Wei, Q., Liu, Y., Ruan, X.: A report on audio tagging with deeper CNN, 1D-convnet and 2D-convnet Google Scholar
  25. 25.
    Xu, K., et al.: General audio tagging with ensembling convolutional neural networks and statistical features. J. Acoust. Soc. Am. 145(6), EL52–EL527 (2019)CrossRefGoogle Scholar
  26. 26.
    Xu, Y., et al.: Unsupervised feature learning based on deep models for environmental audio tagging. IEEE/ACM Trans. Audio Speech Lang. Process. 25(6), 1230–1241 (2017)CrossRefGoogle Scholar
  27. 27.
    Youtube: Accessed 16 July 2019
  28. 28.
    Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)
  29. 29.
    Zissman, M.A.: Comparison of four approaches to automatic language identification of telephone speech. IEEE Trans. Speech Audio Process. 4(1), 31 (1996)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Analytics QuotientBangaloreIndia
  2. 2.Samsung R&D Institute India-BangaloreBangaloreIndia
  3. 3.Birla Institute of Technology and SciencePilaniIndia

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