Quality Enhancement of Low Bit Rate Speech Coder with Nonlinear Prediction

  • Ancy S. AnselamEmail author
  • Sakuntala S. Pillai
  • K. G. Sreeni
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 656)


Toll quality speech codec design with a low bit rate is really a challenging task in modern communication because of the drastic increase in end-users in social networks. Most of the low bit rate speech codecs are based on linear prediction. The code-excited linear prediction codec (CELP) gives good quality decoded speech at a lower bit rate of 4.8 Kbps. But, it neglects the natural nonlinear effects present in speech production process. So, some adaptive techniques are to be used to make the system nonlinear to perform better than linear prediction speech codecs. An adaptive technique with nonlinear prediction of speech, based on truncated Volterra series, is used to generate the nonlinear prediction coefficients. The generated nonlinear prediction coefficients are implemented in G723.1 CELP codec to introduce code-excited nonlinear prediction (CENLP) codec. Advancements in the performance are evaluated using subjective and objective quality measures and compared with the normal G723.1 CELP codec.


CELP Nonlinear long-term prediction Pitch period Prediction gain Volterra series 


  1. 1.
    Makhoul J (1975) Linear prediction: a tutorial review. Proc IEEE 63:561–580CrossRefGoogle Scholar
  2. 2.
    Schroeder M, Atal B (1985) Code-excited linear prediction (CELP): high-quality speech at very low bit rates. In: IEEE international conference on acoustics, speech, and signal processing, vol 10, pp 937–940Google Scholar
  3. 3.
    Chen J-H, Cox RV, Lin YC, Jayant N, Melchner MJ (1992) A low-delay CELP coder for the CCITT 16 Kb/s speech coding standard. IEEE J Sel Areas Commun 10(5):830–849CrossRefGoogle Scholar
  4. 4.
    McCree A, Truong K, George EB, Barnwell TP, Viswanathan V (1996) A 2.4 Kb/s MELP coder candidate for U. S. Federal Standard. In: IEEE international conference on acoustics, speech and signal processing, vol 1, pp 200–203Google Scholar
  5. 5.
    ITU-I Rec. G.729 (1996) Coding of speech at 8 kbps using conjugate-structure algebraic-code-excited linear prediction (CS-ACELP)Google Scholar
  6. 6.
    Despotović V, Perić Z (2013) Design of nonlinear predictors for adaptive predictive coding of speech signals. In: 2013 21st Telecommunications forum (TELFOR), Serbia, Belgrade, 26–28 Nov 2013Google Scholar
  7. 7.
    Despotovic V, Goertz N, Peric Z (2012) Nonlinear long-term prediction of speech based on truncated Volterra series. IEEE Trans Audio Speech Lang Process 20(3):1069–1073CrossRefGoogle Scholar
  8. 8.
    Chu WC (2003) Speech coding algorithms: foundation and evolution of standardized coders. WileyGoogle Scholar
  9. 9.
    Despotovic V, Goertz N, Peric Z (2012) Low-order Volterra longterm predictors. In: Proceedings 10th ITG symposium on speech communication, Braunschweig, Germany, Sept 2012, pp 26–28Google Scholar
  10. 10.
    Despotović V, Görtz N, Perić Z (2012) Improved non-linear long-term predictors based on Volterra filters. Vienna University of Technology, Institute of Telecommunications, Gußhausstr. 25–29, 1040 ViennaGoogle Scholar
  11. 11.
    Anselam AS, Pillai SS (2017) Optimization of code excited linear prediction speech coder with PSVQ-genetic codebook. In: 2017 international conference on wireless communications, signal processing and networking (WiSPNET)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Ancy S. Anselam
    • 1
    Email author
  • Sakuntala S. Pillai
    • 1
  • K. G. Sreeni
    • 2
  1. 1.Department of Electronics and Communication EngineeringMar Baselios College of Engineering and TechnologyThiruvananthapuramIndia
  2. 2.Department of Electronics and Communication EngineeringCollege of Engineering TrivandrumThiruvananthapuramIndia

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