Skip to main content

A Feature Extraction Algorithm for Exoskeleton Speech Control System Based on Noisy Environment

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14268))

Included in the following conference series:

  • 901 Accesses

Abstract

Exoskeleton devices based on speech systems are often affected by noise in the actual working environment, which reduces the recognition rate of speech systems. In order to reduce the influence of noise on the speech system, this paper proposes using joint features of Mel Frequency Cepstrum Coefficients and Gammatone Frequency Cepstrum Coefficients. Then, an improved Fast Correlation-Based Filtering algorithm is used to perform dimensionality reduction and remove irrelevant and redundant features in the joint features to obtain the optimal feature subset. Finally, the experiments show that the feature recognition effect is greatly improved after dimensionality reduction is performed under a low signal-to-noise ratio.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Biswas, M., Rahaman, S., Ahmadian, A., et al.: Automatic spoken language identification using MFCC based time series features. Multimed. Tools Appl. 82, 9565–9595 (2023)

    Article  Google Scholar 

  2. Li, Q., et al.: MSP-MFCC: energy-efficient MFCC feature extraction method with mixed-signal processing architecture for wearable speech recognition applications. IEEE Access 8, 48720–48730 (2020)

    Article  Google Scholar 

  3. Shi, X., Yang, H., Zhou, P.: Robust speaker recognition based on improved GFCC. In: IEEE 2016 2nd IEEE International Conference on Computer and Communications (ICCC), Chengdu, China, pp. 1927–1931 (2016)

    Google Scholar 

  4. Wang, H., Zhang, C.: The application of Gammatone frequency cepstral coefficients for forensic voice comparison under noisy conditions. Aust. J. Forensic Sci. 52, 1–16 (2019)

    MATH  Google Scholar 

  5. Dua, M., Aggarwal, R.K., Biswas, M.: Optimizing integrated features for Hindi automatic speech recognition system. J. Intell. Syst. 29(1), 959–976 (2018)

    Google Scholar 

  6. Dua, M., Aggarwal, R.K., Biswas, M.: Performance evaluation of Hindi speech recognition system using optimized filterbanks. Eng. Sci. Technol. Int. J. 21(3), 389–398 (2018)

    MATH  Google Scholar 

  7. Li, Z., Yao, Q., Ma, W.: Matching subsequence music retrieval in a software integration environment. Complexity 2021, 1–12 (2021)

    MATH  Google Scholar 

  8. Deng, X., Li, M., Wang, L., et al.: RFCBF: enhance the performance and stability of fast correlation-based filter. Int. J. Comput. Intell. Appl. 21(02), 2250009 (2022)

    Article  MATH  Google Scholar 

  9. Zaffar, M., Hashmani, M.A., Savita, K.S., et al.: Role of FCBF feature selection in educational data mining. Mehran Univ. Res. J. Eng. Technol. 39(4), 772–778 (2020)

    Article  MATH  Google Scholar 

  10. Muralishankar, R., Ghosh, D., Gurugopinath, S.: A novel modified Mel-DCT filter bank structure with application to voice activity detection. IEEE Signal Process. Lett. 27, 1240–1244 (2020)

    Article  Google Scholar 

  11. Zhao, X., Shao, Y., Wang, D.L.: CASA-based robust speaker identification. IEEE Trans. Audio Speech Lang. Process. 20(5), 1608–1616 (2012)

    Article  MATH  Google Scholar 

Download references

Acknowledgement

I would like to express my heartfelt thanks and sincere respect to Mr. Wenjie Chen. He has always encouraged me to continue to learn knowledge with his persistent pursuit of science, practical and realistic work attitude, generous and humble approach and selfless dedication. I also thank the National Natural Science Foundation of China (51975002) for its support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wenjie Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Su, Z., Chen, W., Sun, X., Ding, N., Zhi, Y. (2023). A Feature Extraction Algorithm for Exoskeleton Speech Control System Based on Noisy Environment. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14268. Springer, Singapore. https://doi.org/10.1007/978-981-99-6486-4_32

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-6486-4_32

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6485-7

  • Online ISBN: 978-981-99-6486-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics