Skip to main content
SpringerLink
Log in

Deep learning for spoken language identification: Can we visualize speech signal patterns?

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Western countries entertain speech recognition-based applications. It does not happen in a similar magnitude in East Asia. Language complexity could potentially be one of the primary reasons behind this lag. Besides, multilingual countries like India need to be considered so that language identification (words and phrases) can be possible through speech signals. Unlike the previous works, in this paper, we propose to use speech signal patterns for spoken language identification, where image-based features are used. The concept is primarily inspired from the fact that speech signal can be read/visualized. In our experiment, we use spectrograms (for image data) and deep learning for spoken language classification. Using the IIIT-H Indic speech database for Indic languages, we achieve the highest accuracy of 99.96%, which outperforms the state-of-the-art reported results. Furthermore, for a relative decrease of 4018.60% in the signal-to-noise ratio, a decrease of only 0.50% in accuracy tells us the fact that our concept is fairly robust.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Pan S-T, Lan M-L (2014) An efficient hybrid learning algorithm for neural network-based speech recognition systems on FPGA chip. Neural Comput Appl 24(7–8):1879–1885

    Article  Google Scholar 

  2. Mustafa MK, Allen T, Appiah K (2019) A comparative review of dynamic neural networks and hidden Markov model methods for mobile on-device speech recognition. Neural Comput Appl 31(2):891–899

    Article  Google Scholar 

  3. Jun S, Kim M, Oh M, Park H-M (2013) Robust speech recognition based on independent vector analysis using harmonic frequency dependency. Neural Comput Appl 22(7–8):1321–1327

    Article  Google Scholar 

  4. Dua M, Aggarwal R, Biswas M (2018) Discriminatively trained continuous Hindi speech recognition system using interpolated recurrent neural network language modeling. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3499-9

    Article  Google Scholar 

  5. Dudley WH (1939) The vocoder. Bell Labs Rec 18:122

    Google Scholar 

  6. Mukherjee H, Halder C, Phadikar S, Roy K (2017) Read—a Bangla phoneme recognition system. In: Proceedings of the 5th international conference on frontiers in intelligent computing: theory and applications. Springer, pp 599–607

  7. Tang Z, Wang D, Chen Y, Shi Y, Li L (2017) Phone-aware neural language identification. In: 2017 20th conference of the oriental chapter of the international coordinating committee on speech databases and speech I/O systems and assessment (O-COCOSDA). IEEE, pp 1–6

  8. Giwa O, Davel MH (2017) The effect of language identification accuracy on speech recognition accuracy of proper names. In: 2017 Pattern recognition association of South Africa and robotics and mechatronics (PRASA-RobMech). IEEE, pp 187–192

  9. Gunawan TS, Husain R, Kartiwi M (2017) Development of language identification system using MFCC and vector quantization. In: 2017 IEEE 4th international conference on smart instrumentation, measurement and application (ICSIMA). IEEE, pp 1–4

  10. Masumura R, Asami T, Masataki H, Aono Y (2017) Parallel phonetically aware DNNS and LSTM-RNNS for frame-by-frame discriminative modeling of spoken language identification. In: 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 5260–5264

  11. He J, Zhang Z, Zhao X, Li P, Yan Y (2016) Similar language identification for Uyghur and Kazakh on short spoken texts. In: 2016 8th international conference on intelligent human–machine systems and cybernetics (IHMSC), vol 2. IEEE, pp 496–499

  12. Jin M, Song Y, McLoughlin I, Dai L-R (2018) LID-senones and their statistics for language identification. IEEE/ACM Trans Audio Speech Lang Process 26(1):171–183

    Article  Google Scholar 

  13. Mukherjee H, Obaidullah SM, Phadikar S, Roy K (2018) A Dravidian language identification system. In: 2018 24th international conference on pattern recognition (ICPR). IEEE, pp 2654–2657

  14. Gupta M, Bharti SS, Agarwal S (2017) Implicit language identification system based on random forest and support vector machine for speech. In: 2017 4th international conference on power, control & embedded systems (ICPCES).IEEE, pp 1–6

  15. Madhu C, George A, Mary L (2017) Automatic language identification for seven Indian languages using higher level features. In: 2017 IEEE international conference on signal processing, informatics, communication and energy systems (SPICES). IEEE, pp 1–6

  16. Nercessian S, Torres-Carrasquillo P, Martinez-Montes G (2016) Approaches for language identification in mismatched environments. In: 2016 IEEE spoken language technology workshop (SLT). IEEE, pp 335–340

  17. Rebai I, BenAyed Y, Mahdi W (2017) Improving of open-set language identification by using deep SVM and thresholding functions. In: 2017 IEEE/ACS 14th international conference on computer systems and applications (AICCSA). IEEE, pp 796–802

  18. Berkling KM, Arai T, Barnard E (1994) Analysis of phoneme-based features for language identification. In: Proceedings of ICASSP’94. IEEE international conference on acoustics, speech and signal processing, vol 1. IEEE, pp I–289

  19. Srivastava BML, Vydana H, Vuppala AK, Shrivastava M (2017) Significance of neural phonotactic models for large-scale spoken language identification. In: 2017 international joint conference on neural networks (IJCNN). IEEE, pp 2144–2151

  20. Tang Z, Wang D, Chen Y, Li L, Abel A (2018) Phonetic temporal neural model for language identification. IEEE/ACM Trans Audio Speech Lang Process 26(1):134–144

    Article  Google Scholar 

  21. Mukherjee H, Obaidullah SM, Santosh K, Phadikar S, Roy K (2019) A lazy learning-based language identification from speech using MFCC-2 features. Int J Mach Learn Cybern. https://doi.org/10.1007/s13042-019-00928-3

    Article  Google Scholar 

  22. Mukherjee H, Dhar A, Phadikar S, Roy K (2017) RECAL—a language identification system. In: 2017 international conference on signal processing and communication (ICSPC). IEEE, pp 300–304

  23. Watanabe S, Hori T, Hershey JR (2017) Language independent end-to-end architecture for joint language identification and speech recognition. In: 2017 IEEE automatic speech recognition and understanding workshop (ASRU). IEEE, pp 265–271

  24. Revathi A, Jeyalakshmi C, Muruganantham T (2018) Perceptual features based rapid and robust language identification system for various Indian classical languages. In: Computational vision and bio inspired computing. Springer, pp 291–305

  25. Zissman MA, Singer E (1994) Automatic language identification of telephone speech messages using phoneme recognition and n-gram modeling. In: Proceedings of ICASSP’94. IEEE international conference on acoustics, speech and signal processing, vol 1. IEEE, pp I–305

  26. Zissman MA (1995) Language identification using phoneme recognition and phonotactic language modeling. In: 1995 international conference on acoustics, speech, and signal processing, vol 5. IEEE, pp 3503–3506

  27. Saikia R, Singh SR, Sarmah P (2017) Effect of language independent transcribers on spoken language identification for different Indian languages. In: 2017 international conference on Asian language processing (IALP). IEEE, pp 214–217

  28. Lamel LF, Gauvain J-L (1993) Cross-lingual experiments with phone recognition. In: 1993 IEEE international conference on acoustics, speech, and signal processing, vol 2. IEEE, pp 507–510

  29. Ghozi R, Fraj O, Jaïdane M (2007) Visually-based audio texture segmentation for audio scene analysis. In: 2007 15th European signal processing conference. IEEE, pp 1531–1535

  30. Dennis JW. Sound event recognition in unstructured environments using spectrogram image processing. Nanyang Technological University, Singapore

  31. Montalvo A, Costa YM, Calvo JR (2015) Language identification using spectrogram texture. In: Iberoamerican congress on pattern recognition. Springer, pp 543–550

  32. Prahallad K, Kumar EN, Keri V, Rajendran S, Black AW (2012) The IIIT-H Indic speech databases. In: Thirteenth annual conference of the international speech communication association

  33. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436

    Article  Google Scholar 

  34. Zhang D, Han X, Deng C (2018) Review on the research and practice of deep learning and reinforcement learning in smart grids. CSEE J Power Energy Syst 4(3):362–370

    Article  Google Scholar 

  35. Sang J, Yu J, Jain R, Lienhart R, Cui P, Feng J (2018) Deep learning for multimedia: science or technology? In: Proceedings of the 2018 ACM multimedia conference on multimedia conference, ACM, pp 1354–1355

  36. Olivas-Padilla BE, Chacon-Murguia MI (2019) Classification of multiple motor imagery using deep convolutional neural networks and spatial filters. Appl Soft Comput 75:461–472

    Article  Google Scholar 

  37. Chevtchenko SF, Vale RF, Macario V, Cordeiro FR (2018) A convolutional neural network with feature fusion for real-time hand posture recognition. Appl Soft Comput 73:748–766

    Article  Google Scholar 

  38. Wang Y, Chen Y, Yang N, Zheng L, Dey N, Ashour AS, Rajinikanth V, Tavares JMR, Shi F (2019) Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network. Appl Soft Comput 74:40–50

    Article  Google Scholar 

  39. Mukherjee H, Obaidullah SM, Santosh K, Phadikar S, Roy K (2018) Line spectral frequency-based features and extreme learning machine for voice activity detection from audio signal. Int J Speech Technol 21(4):753–760

    Article  Google Scholar 

  40. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10–18

    Article  Google Scholar 

  41. Mohanaiah P, Sathyanarayana P, GuruKumar L (2013) Image texture feature extraction using GLCM approach. Int J Sci Res Publ 3(5):1

    Google Scholar 

  42. Ojala T, Pietikäinen M, Mäenpää T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  43. Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2009) WLD: a robust local image descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720

    Article  Google Scholar 

  44. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

  45. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(Jan):1–30

    MathSciNet  MATH  Google Scholar 

  46. Simons GF, Fennig CD (2017) Ethnologue: languages of Asia. SIL International, Dallas

    Google Scholar 

  47. Bouguelia M-R, Nowaczyk S, Santosh K, Verikas A (2018) Agreeing to disagree: active learning with noisy labels without crowdsourcing. Int J Mach Learn Cybern 9(8):1307–1319

    Article  Google Scholar 

  48. Bhattacharyya S, Snasel V, Dey A, Dey S, Konar D (2018) Quantum spider monkey optimization (QSMO) algorithm for automatic gray-scale image clustering. In: 2018 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 1869–1874

  49. Nath SS, Mishra G, Kar J, Chakraborty S, Dey N (2014) A survey of image classification methods and techniques. In: 2014 international conference on control, instrumentation, communication and computational technologies (ICCICCT). IEEE, pp 554–557

  50. Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, Hoboken

    MATH  Google Scholar 

  51. Das AK, Sengupta S, Bhattacharyya S (2018) A group incremental feature selection for classification using rough set theory based genetic algorithm. Appl Soft Comput 65:400–411

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, West Bengal State University, Kolkata, India

    Himadri Mukherjee & Kaushik Roy

  2. Department of Computer Science and Engineering, Aliah University, Kolkata, India

    Obaidullah Sk Md

  3. Department of Computer Science, The University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  4. Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India

    Santanu Phadikar

  5. CVPR Unit, Indian Statistical Institute, Kolkata, India

    Subhankar Ghosh

  6. Department of Computer Science and Engineering, Future Institute of Engineering and Management, Kolkata, India

    Shibaprasad Sen

Authors
  1. Himadri Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subhankar Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shibaprasad Sen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Obaidullah Sk Md
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. K. C. Santosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Santanu Phadikar
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kaushik Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. C. Santosh.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mukherjee, H., Ghosh, S., Sen, S. et al. Deep learning for spoken language identification: Can we visualize speech signal patterns?. Neural Comput & Applic 31, 8483–8501 (2019). https://doi.org/10.1007/s00521-019-04468-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04468-3

Keywords

Search

Navigation