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Neural Computing and Applications

, Volume 31, Issue 12, pp 8483–8501 | Cite as

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

  • Himadri Mukherjee
  • Subhankar Ghosh
  • Shibaprasad Sen
  • Obaidullah Sk Md
  • K. C. SantoshEmail author
  • Santanu Phadikar
  • Kaushik Roy
Original Article
  • 34 Downloads

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.

Keywords

Language identification Spectrogram Speech pattern Convolutional neural network 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

References

  1. 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):1879–1885CrossRefGoogle Scholar
  2. 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):891–899CrossRefGoogle Scholar
  3. 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):1321–1327CrossRefGoogle Scholar
  4. 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 CrossRefGoogle Scholar
  5. 5.
    Dudley WH (1939) The vocoder. Bell Labs Rec 18:122Google Scholar
  6. 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 599–607Google Scholar
  7. 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 1–6Google Scholar
  8. 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 187–192Google Scholar
  9. 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 1–4Google Scholar
  10. 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 5260–5264Google Scholar
  11. 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 496–499Google Scholar
  12. 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):171–183CrossRefGoogle Scholar
  13. 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 2654–2657Google Scholar
  14. 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 1–6Google Scholar
  15. 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 1–6Google Scholar
  16. 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 335–340Google Scholar
  17. 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 796–802Google Scholar
  18. 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–289Google Scholar
  19. 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 2144–2151Google Scholar
  20. 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):134–144CrossRefGoogle Scholar
  21. 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 CrossRefGoogle Scholar
  22. 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 300–304Google Scholar
  23. 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 265–271Google Scholar
  24. 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 291–305Google Scholar
  25. 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–305Google Scholar
  26. 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 3503–3506Google Scholar
  27. 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 214–217Google Scholar
  28. 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 507–510Google Scholar
  29. 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 1531–1535Google Scholar
  30. 30.
    Dennis JW. Sound event recognition in unstructured environments using spectrogram image processing. Nanyang Technological University, SingaporeGoogle Scholar
  31. 31.
    Montalvo A, Costa YM, Calvo JR (2015) Language identification using spectrogram texture. In: Iberoamerican congress on pattern recognition. Springer, pp 543–550Google Scholar
  32. 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 associationGoogle Scholar
  33. 33.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436CrossRefGoogle Scholar
  34. 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):362–370CrossRefGoogle Scholar
  35. 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 1354–1355Google Scholar
  36. 36.
    Olivas-Padilla BE, Chacon-Murguia MI (2019) Classification of multiple motor imagery using deep convolutional neural networks and spatial filters. Appl Soft Comput 75:461–472CrossRefGoogle Scholar
  37. 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 73:748–766CrossRefGoogle Scholar
  38. 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 74:40–50CrossRefGoogle Scholar
  39. 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):753–760CrossRefGoogle Scholar
  40. 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):10–18CrossRefGoogle Scholar
  41. 41.
    Mohanaiah P, Sathyanarayana P, GuruKumar L (2013) Image texture feature extraction using GLCM approach. Int J Sci Res Publ 3(5):1Google Scholar
  42. 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):971–987CrossRefGoogle Scholar
  43. 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):1705–1720CrossRefGoogle Scholar
  44. 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. 45.
    Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(Jan):1–30MathSciNetzbMATHGoogle Scholar
  46. 46.
    Simons GF, Fennig CD (2017) Ethnologue: languages of Asia. SIL International, DallasGoogle Scholar
  47. 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):1307–1319CrossRefGoogle Scholar
  48. 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 1869–1874Google Scholar
  49. 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 554–557Google Scholar
  50. 50.
    Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, HobokenzbMATHGoogle Scholar
  51. 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 65:400–411CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceWest Bengal State UniversityKolkataIndia
  2. 2.Department of Computer Science and EngineeringAliah UniversityKolkataIndia
  3. 3.Department of Computer ScienceThe University of South DakotaVermillionUSA
  4. 4.Department of Computer Science and EngineeringMaulana Abul Kalam Azad University of TechnologyKolkataIndia
  5. 5.CVPR UnitIndian Statistical InstituteKolkataIndia
  6. 6.Department of Computer Science and EngineeringFuture Institute of Engineering and ManagementKolkataIndia

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