Speech Enhancement Using Wiener Filter Based on Voiced Speech Probability

  • Rashmirekha Ram
  • Abhisek Das
  • Saumendra Kumar Mohapatra
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)


In this digitized world, quality, accuracy and adaptability are more emphasized. Due to immense practical applications, desire for clean signal is highly essential at the user end. In this work, speech signal is considered for enhancement. For this, Wiener filter is proposed based on voiced speech probability (VSP). The probability of the speech signal depends on the performance of voice activity detection (VAD). The decision directed method with likelihood ratio test estimates the noise which improves the performance of VAD. After finding the speech probability, the noise is updated and estimated. The mean square error is optimized by Wiener filter, and the signal is enhanced. For verification and comparison, signal-to-noise ratio (SNR) and perceptual evaluation of speech quality (PESQ) are considered. This proposed method can be utilized in real-time applications.


Speech enhancement Speech presence probability Voice activity detection Minimum mean square error estimation Wiener filter Signal-to-Noise Ratio Perceptual evaluation of speech quality 


  1. 1.
    Quatieri TF (2002) Discrete-time speech signal processing: principle and practice. Prentice Hall, New YorkGoogle Scholar
  2. 2.
    Loizou PC (2013) Speech enhancement: theory and practice. CRC PressGoogle Scholar
  3. 3.
    Ram R, Mohanty MN (2016) Performance analysis of adaptive algorithms for speech enhancement applications. Indian J Sci Technol 9(44):1–9Google Scholar
  4. 4.
    Ram R, Mohanty MN (2017) Design of fractional fourier transform based filter for speech enhancement. Int J Control Theory Appl 10(7):235–243Google Scholar
  5. 5.
    Ke Y, Hu Y, Li J, Zheng C, Li X (2019) A generalized subspace approach for multichannel speech enhancement using machine learning-based speech presence probability estimation, vol 146. Audio Engineering Society Convention, Audio Engineering SocietyGoogle Scholar
  6. 6.
    Upadhyay N, Jaiswal RK (2016) Single channel speech enhancement: using wiener filtering with recursive noise estimation. Procedia Comput Sci 84:22–30 CrossRefGoogle Scholar
  7. 7.
    Khaldi K, Boudraa AO, Turki M (2016) Voiced/unvoiced speech classification-based adaptive filtering of decomposed empirical modes for speech enhancement. IET Signal Process 10:169–180CrossRefGoogle Scholar
  8. 8.
    Chen Z, Hohmann V (2015) Online monaural speech enhancement based on periodicity analysis and a Priori SNR estimation. EEE/ACM Trans Audio Speech Lang Process (TASLP) 23(11):1904–1916Google Scholar
  9. 9.
    Bhowmick A, Chandra M (2017) Speech enhancement using voiced speech probability based wavelet decomposition. Comput and Electr Eng 62:706–718CrossRefGoogle Scholar
  10. 10.
    Lun DPK, Shen TW, Hsung TC, Ho DK (2016) Wavelet based speech presence probability estimator for speech enhancement. Digit Signal Process 22(6):1161–1173MathSciNetCrossRefGoogle Scholar
  11. 11.
    El-Fattah MAA, Dessouky MI, Abbas AM, Diab SM, El-Rabaie ESM, Al-Nuaimy W, … El-Samie FEA (2014) Speech Enhancement with an adaptive wiener filter. Int J Speech Technol 17(1):53–64Google Scholar
  12. 12.
    Sohn J, Kim NS, Sung W (1999) A statistical model-based voice activity detection. IEEE Signal Process Lett 6(1):1–3Google Scholar
  13. 13.
    Ephraim Y, Malah D (1984) Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Trans Acoust, Speech, Signal Process 32(6):1109–1121CrossRefGoogle Scholar
  14. 14.
    Mowlaee P, Scheran D, Stahl J, Wood SU, Kleijn WB (2019) Maximum a posteriori speech enhancement based on double spectrum. Proc. Interspeech 2019:2738–2742Google Scholar
  15. 15.
    Ram R, Mohanty MN (2019) Use of radial basis function network with discrete wavelet transform for speech enhancement. Int J Comput Vis Robot 9(2):207–223CrossRefGoogle Scholar
  16. 16.
    Ram R, Mohanty MN (2018) Performance analysis of adaptive variational mode decomposition approach for speech enhancement. Int J Speech Technol 21(23):69–381CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Rashmirekha Ram
    • 1
  • Abhisek Das
    • 1
  • Saumendra Kumar Mohapatra
    • 1
  1. 1.ITER, Siksha ‘O’ Anusandhan (Deemed to be University)BhubaneswarIndia

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