Skip to main content

An Experimental Study of Continuous Automatic Speech Recognition System Using MFCC with Reference to Punjabi Language

  • Conference paper
  • First Online:
Recent Findings in Intelligent Computing Techniques

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 707))

Abstract

Punjabi language has almost 105 million native speakers and faced the challenge of less resource. The Punjabi ASR system has little research as compared to other Indian languages. This paper examines the continuous vocabulary of Punjabi language using Sphinx toolkit. The proposed work has been implemented on speaker-independent and speaker-dependent speakers in different environmental conditions. The Punjabi ASR system has been trained on 442 phonetically rich sentences using 15 speakers (6 Male and 9 female). The system adopts MFCC at the front end and HMM at the modelling phase to extract and classify feature vectors. The simulation result demonstrates the performance improvement of 93.85% on speaker-dependent dataset and 89.96% on speaker-independent dataset.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kadyan, V., Singh, A., Wadhwa, P.: Hindi dialect (Bangro) spoken language recognition (HD-SLR) system using Sphinx3. In: Proceeding of International Conference on Intelligent Communication, Control and Devices, pp. 991–998 (2017)

    Google Scholar 

  2. Kumar, K., Aggarwal, R.K., Jain, A.: A Hindi speech recognition system for connected words using HTK. Int. J. Comput. Syst. Eng. 1(1), 25–32 (2012)

    Article  Google Scholar 

  3. Aggarwal, R.K., Dave, M.: Application of genetically optimized neural networks for hindi speech recognition system. In: World Congress on Information and Communication Technologies, pp. 512–517 (2011)

    Google Scholar 

  4. Aggarwal, R.K., Dave, M.: Using Gaussian mixtures for Hindi speech recognition system. Int. J. Signal Process. Image Process. Pattern Recogn. 4(4), 157–170 (2011)

    Google Scholar 

  5. Aggarwal, R.K., Dave, M.: Fitness evaluation of Gaussian mixtures in Hindi speech recognition system. In: First International Conference on Integrated Intelligent Computing, pp. 177–183 (2010)

    Google Scholar 

  6. Tailor, J.H., Shah, D.B.: Speech recognition system architecture for Gujarati language. Int. J. Comput. Appl. 138(12), 28–31 (2016)

    Google Scholar 

  7. Sangeetha, J., Jothilakshmi, S.: Automatic continuous speech recogniser for Dravidian languages using the auto associative neural network. Int. J. Comput. Vis. Robot. 6(1–2), 113–126 (2016)

    Article  Google Scholar 

  8. Mannepalli, K., Sastry, P.N., Suman, M.: MFCC-GMM based accent recognition system for Telugu speech signals. Int. J. Speech Technol. 19(1), 87–93 (2016)

    Article  Google Scholar 

  9. Mannepalli, K., Sastry, P.N., Rajesh, V.: Accent detection of Telugu speech using prosodic and formant features. In: International Conference on Signal Processing and Communication Engineering Systems (SPACES), pp. 318–322 (2015)

    Google Scholar 

  10. Ghai, W., Singh, N.: Continuous speech recognition for Punjabi language. Int. J. Comput. Appl. 72(14) (2013)

    Article  Google Scholar 

  11. Dua, M., Aggarwal, R.K., Kadyan, V., Dua, S.: Punjabi automatic speech recognition using HTK. IJCSI Int. J. Comput. Sci. Iss. 9(4), 1694–0814 (2012)

    Google Scholar 

  12. Dua, M., Aggarwal, R.K., Kadyan, V., Dua, S.: Punjabi speech to text system for connected words, pp. 206–209 (2012)

    Google Scholar 

  13. Kumar, R., Singh, M.: Spoken isolated word recognition of Punjabi language using dynamic time warp technique. In: Information Systems for Indian Languages, pp. 301–301 (2011)

    Chapter  Google Scholar 

  14. Bharali, SrutiSruba, Kalita, Sanjib Kr: A comparative study of different features for isolated spoken word recognition using HMM with reference to Assamese language. Int. J. Speech Technol. 18(4), 673–684 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nancy Bassan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bassan, N., Kadyan, V. (2019). An Experimental Study of Continuous Automatic Speech Recognition System Using MFCC with Reference to Punjabi Language. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 707. Springer, Singapore. https://doi.org/10.1007/978-981-10-8639-7_28

Download citation

Publish with us

Policies and ethics