Skip to main content

Advertisement

SpringerLink
Log in

FPGA-Based Robust Wireless Speech Motion Control for Home Service Robot Subject to Environmental Noises

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

In this paper, a robust speech recognition system for the recognition of the speeches subjected to environmental noise is designed and implemented on FPGA to control a home service robot wirelessly. An empirical mode decomposition is used to separate the clean speeches from the speech signals contaminated by environmental noise. To improve the recognition speed, instead of continuous hidden Markov model (CHMM), Discrete HMM (DHMM) is used here to reduce the computation load during speech recognition. However, to compensate the decreased speech recognition rate using DHMM, this paper uses fuzzy vector quantization (FVQ) on the modeling of DHMM to improve the speech recognition rates. It will be shown that the computation time just increases a little, while the speech recognition rates increase much when the FVQ is applied. Finally, combining a wireless module, a FPGA-based speech recognition system is designed to control the motions of a home service robot wirelessly via speech commands under some environmental noises. The performance of the designed system will be demonstrated in the end of this paper.

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27

Similar content being viewed by others

References

  1. Ding, I.J.: Fuzzy rule-based system for decision making support of hybrid SVM-GMM acoustic event detection. Int. J. Fuzzy Syst. 14(1), 118–130 (2012)

    Google Scholar 

  2. Buera, L., Miguel, A., Saz, O., Ortega, A., Lleida, E.: Unsupervised data-driven feature vector normalization with acoustic model adaptation for robust speech recognition. In: IEEE Transactions on Audio, Speech, and Language Processing, vol. 18, no. 2, pp. 296–309 (2010)

  3. Mitra, V., Franco, H., Graciarena, M., Vergyri, D.: Medium-duration modulation cepstral feature for robust speech recognition. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1749–1753, 4–9 May 2014

  4. Yeh, H.G., Raveendran, P., Jamuar, S.S.: Robust speech recognition using harmonic features. IET Signal Proc. 8(2), 167–175 (2014)

    Article  Google Scholar 

  5. Kim, J., You, B.J.: Fault detection in a microphone array by intercorrelation of features in voice activity detection. IEEE Trans. Ind. Electron. 58(6), 2568–2571 (2011)

    Article  Google Scholar 

  6. Zhan, Y., Leung, H., Kwak, K.C., Yoon, H.: Automated speaker recognition for home service robots using genetic algorithm and dempster–shafer fusion technique. In: IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 9, pp. 3058–3068 (2009)

  7. Ishi, C.T., Matsuda, S., Kanda, T., Jitsuhiro, T., Ishiguro, H., Nakamura, S., Hagita, N.: A robust speech recognition system for communication robots in noisy environments. IEEE Trans. Robot. 24(3), 759–763 (2008)

    Article  Google Scholar 

  8. Petkov, P.N., Henter, G.E., Kleijn, W.B.: Maximizing phoneme recognition accuracy for enhanced speech intelligibility in noise. In: IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 5, pp. 1035–1045 (2013)

  9. Siniscalchi, S.M., Yu, D., Deng, L., Lee, C.H.: Speech recognition using long-span temporal patterns in a deep network model. IEEE Signal Process. Lett. 20(3), 201–204 (2013)

    Article  Google Scholar 

  10. Lee, C.H.: On stochastic feature and model compensation approaches to robust speech recognition. Speech Commun. 25, 29–47 (1998)

    Article  Google Scholar 

  11. Gales, M.J.F., Young, S.J.: Robust speech recognition in additive and convolutional noise using parallel model combination. Comput. Speech Lang. 9, 289–307 (1995)

    Article  Google Scholar 

  12. Tsao, Y., Lee, C.H.: An ensemble speaker and speaking environment modeling approach to robust speech recognition. In: IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 5, pp. 1025–1037 (2009)

  13. Windmann, S., Haeb-Umbach, R.: Parameter estimation of a state-space model of noise for robust speech recognition. In: IEEE Transactions on Audio, Speech, and Language Processing, vol. 17, no. 8, pp. 1577–1590 (2009)

  14. Pan, S.T., Li, X.Y.: An FPGA-based embedded robust speech recognition system designed by combining EMD and a genetic algorithm. IEEE Trans. Instrum. Meas. 61(9), 2560–2572 (2012)

    Article  Google Scholar 

  15. Li, X., Zou, X., Zhang, R., Liu, G.: Method of speech enhancement based on Hilbert-Huang transform. In: 7th World Congress on Intelligent Control and Automation, pp. 8419–8424, 25–27 June 2008

  16. Wang, W., Li, X., Zhang, R.: Speech detection based on hilbert-huang transform. In: First International Multi-Symposiums on Computer and Computational Sciences, vol. 1, pp. 290–293, 20–24 June 2006

  17. Rosero, J.A., Romeral, L., Ortega, J.A., Rosero, E.: Short-circuit detection by means of empirical mode decomposition and wigner-ville distribution for PMSM running under dynamic condition. IEEE Trans. Ind. Electron. 56(11), 4534–4547 (2009)

    Article  Google Scholar 

  18. Lee, M.H., Shyu, K.K., Lee, P.L., Huang, C.M., Chiu, Y.J.: Hardware implementation of EMD using DSP and FPGA for online signal processing. IEEE Trans. Ind. Electron. 58(6), 2473–2481 (2011)

    Article  Google Scholar 

  19. Molla, M.K.I., Hirose, K.: Single-mixture audio source separation by subspace decomposition of hilbert spectrum. In: IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 15, no. 3, pp. 893–900 (2007)

  20. Huang, C.F., Chang, B.R., Cheng, D.W., Chang, C.H.: Feature selection and parameter optimization of a fuzzy-based stock selection model using genetic algorithms. Int. J. Fuzzy Syst. 14(1), 65–75 (2012)

    MathSciNet  Google Scholar 

  21. Goharimanesh, M., Lashkaripour, A., Shariatnia, S., Akbari, A.: Diabetic control using genetic fuzzy-PI controller. Int. J. Fuzzy Syst. 16(2), 133–139 (2014)

    MathSciNet  Google Scholar 

  22. Shi, Y., Liu, J., Liu, R.: Single-chip speech recognition system based on 8051 microcontroller core. IEEE Trans. Consum. Electron. 47(1), 149–153 (2001)

    Article  Google Scholar 

  23. Cheng, O., Abdulla, W., Salcic, Z.: Hardware–software codesign of automatic speech recognition system for embedded real-time applications. IEEE Trans. Ind. Electron. 58(3), 850–859 (2011)

    Article  Google Scholar 

  24. Tsai, C.C., Huang, H.C., Lin, S.C.: FPGA-based parallel DNA algorithm for optimal configurations of an omnidirectional mobile service robot performing fire extinguishment. IEEE Trans. Ind. Electron. 58(3), 1016–1026 (2011)

    Article  Google Scholar 

  25. Pan, S.T.: Fuzzy vector quantization on the modeling of discrete hidden markov model for speech recognition. Int. J. Fuzzy Syst. 13(2), 130–139 (2011)

    Google Scholar 

  26. Blunsom, P.: Hidden Markov model. The University of Melbourne, Department of Computer Science and Software Engineering, 19 Aug 2004

  27. Mathews, J.H., Fink, K.D.: Numerical Methods Using MATLAB, 4th edn. Prentice-Hall, Upper Saddle River (2004)

    Google Scholar 

  28. Hirsch, H.G., Pearce, D.: The aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions. In: Proceedings of the ISCA ITRW ASR2000, Sept 2000

Download references

Acknowledgments

This research work was supported by the National Science Council of the Republic of China under contract NSC 104-2221-E-390-021.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Kaohsiung, No. 700, Kaohsiung, University Rd., Nanzih Dis., Kaohsiung, 811, Taiwan, ROC

    Shing-Tai Pan

  2. Department of Electrical Engineering, Chung Yuan Christian University, Jhongli, 320, Taiwan, ROC

    Cheng-Yuan Chang

  3. Graduate Institute of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan, ROC

    Yi-Heng Tsai

Authors
  1. Shing-Tai Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cheng-Yuan Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi-Heng Tsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shing-Tai Pan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, ST., Chang, CY. & Tsai, YH. FPGA-Based Robust Wireless Speech Motion Control for Home Service Robot Subject to Environmental Noises. Int. J. Fuzzy Syst. 19, 925–941 (2017). https://doi.org/10.1007/s40815-016-0222-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-016-0222-9

Keywords

Search

Navigation