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A Simplified Realization of Zero Frequency Filter for Hardware Implementation

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Abstract

Zero frequency filtering (ZFF) is a well-explored technique for detecting glottal closure instants (GCIs) from the speech signal. The features extracted from the ZFF of the speech signal have also been used for many speech-based applications. Zero frequency resonators used in the infinite impulse response (IIR) realization of ZFF are unstable filters. Consequently, the filter output grows or decays in an unbounded manner. In the finite impulse response (FIR) realization of ZFF, the filter instability problem is address by using more adders and multipliers. In this paper, a simplified stable IIR realization of ZFF (SIIR-ZFF) is proposed for efficient hardware implementation. The SIIR-ZFF requires significantly less hardware than the earlier reported IIR-ZFF as well as FIR-ZFF. Performance of the proposed approach for the task of detecting GCIs from speech signal is found to be almost equivalent to that of the conventional IIR-ZFF. The hardware architecture for the proposed approach is designed and implemented on FPGA.

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References

  1. V.L.R. da Costa, H.V. Schettino, Â. Camponogara, F.P. de Campos, M.V. Ribeiro, Digital filters for clustered-OFDM-based PLC systems: design and implementation. Digit. Signal Proc. 70, 166–177 (2017)

    Article  Google Scholar 

  2. K.T. Deepak, B.D. Sarma, S.R.M. Prasanna, Foreground speech segmentation using zero frequency filtered signal, in INTERSPEECH (2012), pp. 1512–1515

  3. K.T. Deepak, S. Prasanna, Epoch extraction using zero band filtering from speech signal. Circuits Syst. Signal Process. 34(7), 2309–2333 (2015)

    Article  Google Scholar 

  4. N. Dhananjaya, B. Yegnanarayana, Voiced/nonvoiced detection based on robustness of voiced epochs. IEEE Signal Process. Lett. 17(3), 273–276 (2010)

    Article  Google Scholar 

  5. S.H. Dumpala, K.V. Sridaran, S.V. Gangashetty, B. Yegnanarayana, Analysis of laughter and speech-laugh signals using excitation source information, in Acoustics, Speech and Signal Processing (2014), pp. 975–979

  6. P. Gangamohan, S.R. Kadiri, B. Yegnanarayana, Analysis of emotional speech at subsegmental level, in INTERSPEECH (2013), pp. 1916–1920

  7. P. Gangamohan, B. Yegnanarayana, A robust and alternative approach to zero frequency filtering method for epoch extraction, in Proceedings of the INTERSPEECH (2017), pp. 2297–2300

  8. D. Govind, S. Prasanna, Epoch extraction from emotional speech, in International Conference on Signal Processing and Communications (SPCOM) (2012), pp. 1–5

  9. J. Kominek, A.W. Black, The CMU arctic speech databases, in Fifth ISCA Workshop on Speech Synthesis (2004)

  10. K.S. Kumar, M.S.H. Reddy, K.S.R. Murty, B. Yegnanarayana, Analysis of laugh signals for detecting in continuous speech, in Proceedings of the INTERSPEECH (2009), pp. 1591–1594

  11. M. Lopez-Ramirez, L.M. Ledesma-Carrillo, E. Cabal-Yepez, G. Botella, C. Rodriguez-Donate, S. Ledesma, FPGA-based methodology for depth-of-field extension in a single image. Digit. Signal Proc. 70, 14–23 (2017)

    Article  Google Scholar 

  12. V.K. Mittal, B. Yegnanarayana, Study of changes in glottal vibration characteristics during laughter, in INTERSPEECH (2014), pp. 1777–1781

  13. V.K. Mittal, B. Yegnanarayana, Effect of glottal dynamics in the production of shouted speech. J. Acoust. Soc. Am. 133(5), 3050–3061 (2013)

    Article  Google Scholar 

  14. K.S.R. Murthy, B. Yegnanarayana, Epoch extraction from speech signals. IEEE Trans. Audio Speech Lang. Process. 16(8), 1602–1613 (2008)

    Article  Google Scholar 

  15. K.S.R. Murty, B. Yegnanarayana, M.A. Joseph, Characterization of glottal activity from speech signals. IEEE Signal Process. Lett. 16(6), 469–472 (2009)

    Article  Google Scholar 

  16. B. Pattanayak, J.K. Rout, G. Pradhan, Adaptive spectral smoothening for development of robust keyword spotting system. IET Signal Process. 13, 544–550 (2019)

    Article  Google Scholar 

  17. G. Pradhan, S.R.M. Prasanna, Speaker verification by vowel and nonvowel like segmentation. IEEE Trans. Audio Speech Lang. Process. 21(4), 854–867 (2013)

    Article  Google Scholar 

  18. G. Pradhan, B. Haris, S.R.M. Prasanna, R. Sinha, Speaker verification in sensor and acoustic environment mismatch conditions. Int. J. Speech Technol. 15(3), 381–392 (2012)

    Article  Google Scholar 

  19. S.R.M. Prasanna, D. Govind, K.S. Rao, B. Yegnanarayana, Fast prosody modification using instants of significant excitation, in Proceedings of the Speech Prosody (2010)

  20. S.R.M. Prasanna, G. Pradhan, Significance of vowel-like regions for speaker verification under degraded conditions. IEEE Trans. Audio Speech Lang. Process. 19(8), 2552–2565 (2011)

    Article  Google Scholar 

  21. K.S. Rao, B. Yegnanarayana, Prosody modification using instants of significant excitation. IEEE Trans. Audio Speech Lang. Process. 14(3), 972–980 (2006)

    Article  Google Scholar 

  22. G. Seshadri, B. Yegnanarayana, Performance of an event-based instantaneous fundamental frequency estimator for distant speech signals. IEEE Trans. Audio Speech Lang. Process. 19(7), 1853–1864 (2011)

    Article  Google Scholar 

  23. S. Shahnawazuddin, N. Adiga, H.K. Kathania, Effect of prosody modification on children’s ASR. IEEE Signal Process. Lett. 24(11), 1749–1753 (2017)

    Article  Google Scholar 

  24. S. Shahnawazuddin, N. Adiga, H.K. Kathania, G. Pradhan, R. Sinha, Studying the role of pitch-adaptive spectral estimation and speaking-rate normalization in automatic speech recognition. Digit. Signal Proc. 79, 142–151 (2018)

    Article  MathSciNet  Google Scholar 

  25. N. Srinivas, G. Pradhan, P.K. Kumar, FPGA implementation of zero frequency filter, in Conference on Information and Communication Technology (CICT) (2018), pp. 1–5

  26. N. Srinivas, K. Srinivas, G. Pradhan, P.K. Kumar, FPGA implementation for real-time epoch extraction in speech signal, in International Conference on Advances in Computing and Data Sciences (Springer, 2018), pp. 392–400

  27. K.S. Srinivas, K. Prahallad, An FIR implementation of zero frequency filtering of speech signals. IEEE Trans. Audio Speech Lang. Process. 20(9), 2613–2617 (2012)

    Article  Google Scholar 

  28. N. Srinivas, G. Pradhan, P.K. Kumar, Detection of vowel-like speech: an efficient hardware architecture and it’s FPGA prototype. Microsyst. Technol. 25, 1333–1343 (2018)

    Article  Google Scholar 

  29. N. Srinivas, G. Pradhan, P.K. Kumar, An efficient hardware architecture for detection of vowel-like regions in speech signal. Integration 63, 185–195 (2018)

    Article  Google Scholar 

  30. S.A. Thati, K.S. Kumar, B. Yegnanarayana, Synthesis of laughter by modifying excitation characteristics. J. Acoust. Soc. Am. 133(5), 3072–3082 (2013)

    Article  Google Scholar 

  31. A. Varga, H.J.M. Steeneken, Assessment for automatic speech recognition: II. NOISEX-92: a database and an experiment to study the effect of additive noise on speech recognition systems. Speech Commun. 12(3), 247–251 (1993)

    Article  Google Scholar 

  32. A. Vuppala, J. Yadav, S. Chakrabarti, K.S. Rao, Vowel onset point detection for low bit rate coded speech. IEEE Trans. Audio Speech Lang. Process. 20(6), 1894–1903 (2012)

    Article  Google Scholar 

  33. J. Yadav, K.S. Rao, Detection of vowel offset point from speech signal. IEEE Signal Process. Lett. 20(4), 299–302 (2013)

    Article  Google Scholar 

  34. B. Yegnanarayana, S.R.M. Prasanna, Analysis of instantaneous \(f_{0}\) contours from two speakers mixed signal using zero frequency filtering, in Acoustics Speech and Signal Processing (2010), pp. 5074–5077

  35. B. Yegnanarayana, K.S.R. Murty, Event-based instantaneous fundamental frequency estimation from speech signals. IEEE Trans. Audio Speech Lang. Process. 17(4), 614–624 (2009)

    Article  Google Scholar 

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Acknowledgements

This research work is a sub-module of the project “Development of Speech Based Person Authentication System in FPGA” under SMDP-C2SD (9(I)/2014-MDD) program and is supported by the Ministry of Electronic and Information Technology (MeitY), Government of India.

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Correspondence to Gayadhar Pradhan.

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Srinivas, N., Pradhan, G. & Govind, D. A Simplified Realization of Zero Frequency Filter for Hardware Implementation. Circuits Syst Signal Process 39, 4717–4729 (2020). https://doi.org/10.1007/s00034-020-01369-y

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