Skip to main content
SpringerLink
Log in

Chinese dialect tone’s recognition using gated spiking neural P systems

  • Regular Paper
  • Published:
Journal of Membrane Computing Aims and scope Submit manuscript

Abstract

Tone is the changing trend of pitch with time. In Chinese, tone plays an essential role for distinguishing meaning. Chinese dialect’s tone is more complex with Mandarin. In the field of Chinese dialect phonetics research, using human earing to recognize the types of tones is still the main method. So batch processing is not possible. In this paper, we construct a GSNP (gated spiking neural P) model with 2 layers which can process time series data to recognize the tones of Chinese dialects. The average accuracy rate of seven cities’ speech is more than 97%. Even in the case of small training samples, compared with other methods, the GSNP model has simpler structure, higher accuracy and more efficiency. It can not only improve the work efficiency of Chinese dialect field investigation, but also help researchers to screen the sounds with special sounds.

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

Similar content being viewed by others

References

  1. Liu, F. (1924). Record of Experiments on the Four Tones. Shanghai: Yiqun Publishing House.

    Google Scholar 

  2. Wang, L. (2014). Une Prononciation Chinoise de Po-Pei. Beijing: China Publishing House.

    Google Scholar 

  3. Zhao, Y. (1980). Language Problems. Beijing: The Commercial Press.

    Google Scholar 

  4. Chang, P. C., Sun, S. W., & Chen, S. H. (1990). Mandarin tone recognition by multi-layer perceptron, International Conference on Acoustics.

  5. Zhao, L., Zou, C., & Wu, Z. (2000). A tone recognition method for Continuous Chinese Speech based on continuous distributed HMM. Signal Processing, 16(1), 20–23.

    Google Scholar 

  6. Xie, Z., Miao, Z., & Geng, J. (2010). Tone Recognition of Mandarin Speech using BP Neural Network, 2010 International Conference on Image Analysis and Signal Processing, Beijing.

  7. Fu, D., Li, S., & Wang, S. (2010). Tone recognition based on support vector machine in continuous mandarin Chinese. Computer Science, 37(5), 228–230.

    Google Scholar 

  8. Chao, H., Yang, Z., & Liu, W. (2012). Improved tone modeling by exploiting articulatory features for mandarin speech recognition. Journal of Computer Applications, 33(10), 2939–2944.

    Article  Google Scholar 

  9. Tan, Y., Liu, W., Jiang, W., & Zheng, H. (2015). Integration of articulatory knowledge and voicing features based on DNNHMM for Mandarin speech recognition, International Joint Conference on Neural Networks, IEEE.

  10. Lin, J., Xie, Y., & Zhang, J. (2017). Improving Mandarin Tone Recognition Based on DNN by Combining Acoustic and Articulatory Features. IEEE: International Symposium on Chinese Spoken Language Processing.

  11. Ryant, N., Yuan, J., & Liberman, M. (2014). Mandarin tone classification without pitch tracking, IEEE International Conference on Acoustics. IEEE.

  12. Shen, L., & Wang, W. (2018). Fusion feature based automatic Chinese short tone classification. Technical Acoustics, 37(2), 71–78.

    Google Scholar 

  13. Howie, J. M. (2009). On the domain of tones in mandarin. Phonetics, 30(3), 129–148.

    Article  Google Scholar 

  14. Zhao, X. (2014). A study of the tone of Chinese vowels recognition based on spectrogram. Changchun: DongBei Normal University.

    Google Scholar 

  15. Li, Y., Fan, X., & Yang, H. (2017). Application of spectrogram for Mandarins tone recognition. Information’s Communications, 175(7), 89–92.

    Google Scholar 

  16. Roy, K., Jaiswal, A., & Panda, P. (2019). Towards spike-based machine intelligence with neuromorphic computing. Nature, 575, 607–617.

    Article  Google Scholar 

  17. Zhang, F., Rong, H., Neri, F., & Pérez-Jiménez, M. J. (2014). An optimization spiking neural P system for approximately solving combinatorial optimization problems. International Journal of Neural Systems, 24(5), 1440006–16.

    Article  Google Scholar 

  18. Zhang, G., Rong, H., Paul, P., He, Y., Neri, F., & Pérez-Jiménez, M. J. (2021). A complete arithmetic calculator constructed from spiking neural P systems and its application to information fusion. International Journal of Neural Systems, 31(1), 1–17.

    Article  Google Scholar 

  19. Manca, V., & Bianco, L. (2008). Biological networks in metabolic P systems. Bio Systems, 91(3), 489–498.

    Article  Google Scholar 

  20. Romero-Campero, F. J., & Pérez-Jiménez, M. J. (2008). Modelling gene expression control using P systems: the Lac Operon, a case study. Biosystems, 91(3), 438–457.

    Article  Google Scholar 

  21. Perez-Jimenez, M. J., & Romero-Campero, F. J. (2005). A Study of the Robustness of the EGFR Signalling Cascade Using Continuous Membrane Systems. Mechanisms, Symbols, and Models Underlying Cognition, Lecture Notes in Computer Science, 3561.

  22. Nagy, B., & Szegedi, L. (2006). Membrane computing and graphical operating systems. Journal of Universal Computer Science, 12(9), 1312–1331.

    Google Scholar 

  23. Ceterchi, R., Gramatovici, R., Jonoska, N., Subramanian, K. G. (2003). Tissue-like P systems with active membranes for picture generation. Fundamenta Informaticae, 56(4), 311–328.

    MATH  Google Scholar 

  24. Enguix, G. B. (2014). Unstable P Systems: Applications to Linguistics, International Conference on Membrane Computing (vol. 3365, pp. 190–209), Berlin.

  25. Enguix, G. B., Gramatovici, R. (2004). Parsing with active P automata: Lecture Notes in Computer Science. pp. 31–42.

  26. Yuan, G., & Gu, X. (2010). A Differential Evaluation Based Membrane Computing Algorithm. Shanghai, China: Annual Academic Meeting of Shanghai Society of chemistry and chemical engineering.

  27. Păun, G., & Păun, R. A. (2005). Membrane computing as a framework for modeling economic processes. Timisoara, Romania: International Symposium on Symbolic Numeric Algorithms for Scientific Computing.

  28. Păun, G., & Păun, R. (2006). Membrane computing and economics: numerical P systems. Fundamenta Informaticae, 73(1/2), 213–227.

    MATH  Google Scholar 

  29. Guo, D., Xia, H., & Zhou, Y. (2013). A new optimized algorithm for solving nonlinear equations based on membrane computing. Computer Applications and Software, 30(2), 165–167.

    Google Scholar 

  30. Besozzi, D., Cazzaniga, P., Pescini, D., & Mauri, G. (2008). Modelling metapopulations with stochastic membrane systems. Biosystems, 91(3), 499–514.

    Article  Google Scholar 

  31. Colomer, M., Margalida, A., Sanuy, D., & Pérez-Jiménez, M. J. (2011). A bio-inspired computing model as a new tool for modeling ecosystems: The avian scavengers as a case study. Ecological Modelling, 222(1), 33–47.

    Article  Google Scholar 

  32. Ionescu, M. F., Pun, G., & Yokomori, T. (2006). Spiking neural P systems. Fundamenta Informaticae, 71(2), 279–308.

    MATH  Google Scholar 

  33. Păun, G. (2008). Spiking neural P systems with astrocyte-like control. Journal of UCS, 13(11), 1707–1721.

    Google Scholar 

  34. Păun, G., & Pan, L. (2009). Spiking neural P systems with anti-spikes. International Journal of Computers Communications and Control, 4(3), 273–282.

    Article  Google Scholar 

  35. Liu, Y., et al. (2022). Spiking neural P systems with membrane potentials, inhibitory rules, and anti-spikes. Entropy, 834, 1–24.

    Google Scholar 

  36. Pan, L., Păun, G., Zhang, G., & Neri, F. (2017). Spiking neural P systems with communication on request. International Journal of Neural Systems, 27, 1750042.

    Article  Google Scholar 

  37. Dong, J., Zhang, G., Luo, B., Yang, Q., Guo, D., Rong, H., Zhu, M., Zhou, K. (2022). A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems. Information Sciences, 596, 1–14.

    Article  Google Scholar 

  38. Peng, H., Yang, J., Wang, J., Wang, T., Sun, Z., Song, X., Luo, X., & Huang, X. (2017). Spiking neural P systems with multiple channels. Neural Networks the Official Journal of the International Neural Network Society, 66.

  39. Peng, H., Wang, J., Pérez-Jiménez, M. J., & Riscos-Núñez, A. (2019). Dynamic threshold neural P systems. Knowledge-Based Systems, 163(1), 875–884.

    Article  Google Scholar 

  40. Peng, H., & Wang, J. (2019). Coupled neural P systems. IEEE Transactions on Neural Networks and Learning Systems, 30(6), 1672–1682.

    Article  Google Scholar 

  41. Zhao, S., Zhang, L., Liu, Z., Peng, H., & Wang, J. (2022). ConvSNP: a deep learning model embedded with SNP-like neurons. Journal of Membrane Computing, 4, 87–95.

    Article  Google Scholar 

  42. Peng, H., Bao, T., Luo, X., Wang, J., & Pérez-Jiménez, M. J. (2020). Dendrite P systems. Neural Networks, 127, 110–120.

    Article  MATH  Google Scholar 

  43. Song, T., Pan, L., & Păun, G. (2014). Spiking neural P systems with rules on synapses. Theoretical Computer Science, 529, 82–95.

    Article  MATH  Google Scholar 

  44. Wu, T., Păun, A., Zhang, Z., & Pan, L. (2017). Spiking neural P systems with polarizations. IEEE Transactions on Neural Networks and Learning Systems, 99, 1–12.

    Google Scholar 

  45. Peng, H., Li, B., Wang, J., Song, X., & Pérez-Jiménez, M. J. (2019). Spiking neural P systems with inhibitory rules. Knowledge-Based Systems, 188, 105064.

    Article  Google Scholar 

  46. Song, X., Valencia-Cabrera, L., Peng, H., Wang, J., & Pérez-Jiménez, M. J. (2020). Spiking neural P systems with delay on synapses. International Journal of Neural Systems, 31(2), 2050042.

    Google Scholar 

  47. Liu, Q., Long, L., Peng, H., Wang, J., Yang, Q., Song, X., Riscos-Núñez, A., & Pérez-Jiménez, M. J. (2021). Gated spiking neural P systems for time series forecasting. IEEE Transactions on Neural Networks and Learning Systems 1–10.

  48. Karpathy, A., Johnson, J., & Li, F. (2015). Visualizing and understanding recurrent networks, https://doi.org/10.48550/arXiv.1506.02078.

Download references

Acknowledgements

This work was supported by the Ministry of education of Humanities and Social Science project (Grant No. 19YJCZH244), the National Natural Science Foundation of China (Grant Nos. 61876101, 61802234, 61806114 ).

Author information

Author notes

  1. Xiyu Liu and Yanmei Shao have contributed equally to this work.

Authors and Affiliations

  1. Academy of Management Science, Business School, Shandong Normal University, DaXue Road, Jinan, 25000, Shandong, China

    Hongyan Zhang & Xiyu Liu

  2. School of Chinese Language and Literature, Shandong Normal University, DaXue Road, Jinan, 25000, Shandong, China

    Yanmei Shao

Authors
  1. Hongyan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiyu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanmei Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongyan Zhang.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, H., Liu, X. & Shao, Y. Chinese dialect tone’s recognition using gated spiking neural P systems. J Membr Comput 4, 284–292 (2022). https://doi.org/10.1007/s41965-022-00113-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41965-022-00113-6

Keywords

Search

Navigation