Kun-Ching Wang


  1. [1] D.K. Freeman, G. Cosier, C.B. Southcott, and I. Boyd, Thevoice activity detector for the pan European digital cellularmobile telephone service, Proc. ICASSP’89, 1989, 369–372.
  2. [2] J. Sohn, N.S. Kim, and W. Sung, A statistical model-basedvoice activity detection, IEEE Signal Processing Letter, 6,1999, 1–3.
  3. [3] M. Marzinzik and B. Kollmeier, Speech pause detection fornoise spectrum estimation by tracking power envelop dynamics,IEEE Transactions on Speech and Audio Processing, 10, 2002,109–118.
  4. [4] L. Karray and A. Martin, Towards improving speech detectionrobustness for speech recognition in adverse environment,Speech Communication 40, 2003, 261–276.
  5. [5] K. Itoh and M. Mizushima, Environmental noise reductionbased on speech/non-speech identification for hearing aids,Proc. IEEE ICASSP, 1, 1997, 419–422.
  6. [6] O. Tanrikulu, B. Baykal, A.G. Constantinides, and J.A. Cham-bers, Critically sampled sub-band acoustic echo cancellersbased on IIR and FIR filter banks, IEEE Transactions onSignal Proccessing, 45 (4), 1997, 901–912.
  7. [7] J.R. Deller, J.G. Proakis, and J.H.L. Hansen, Discrete-timeprocessing of speech signals (New York, NY: Macmillan, 1993).
  8. [8] J. Sohn and W. Sung, A voice activity detector employingsoft decision based noise spectrum adaptation, Proc. IEEEICASSP, 1, 1998, 365–368.
  9. [9] F. Beritelli, S. Casale, and A. Cavallaro, A robust voice activitydetector for wireless communications using soft computing,IEEE Journal on Selected Areas in Communication, 16, 1998,1818–1829.
  10. [10] Y.D. Cho and A. Kondoz, Analysis and improvement of astatistical model-based voice activity detector, IEEE SignalProcessing Letters, 8, 2001, 276–278.
  11. [11] E. Nemer, R. Goubran, and S. Mahmoud, Robust voice activitydetection using higher-order statistics in the LPC residualdomain, IEEE Transactions on Speech and Audio Processing,9, 2001, 217–231.
  12. [12] J.H. Chang, N.S. Kim, and S.K. Mitra, Voice activity detectionbased on multiple statistical models, IEEE Transactions onSignal Processing, 54 (6), 2006, 1965–1976.
  13. [13] J. Ramirez, P. Yelamos, J.M. Gorriz, J.C. Segura, and L.Garcia, Speech/non-speech discrimination combining advancedfeature extraction and SVM learning, Proc. Int. Conf. SpokenLanguage Processing, 2006, 1662–1665.
  14. [14] A. Benyassine, E. Shlomot, H.Y. Su, D. Massaloux,C. Lamblin, and J.P. Petit, ITU-T recommendation G.729Annex B: a silence compression scheme for use with G.729optimized for V.70 digital simultaneous voice and data appli-cations, IEEE Communications Magazine, 35, 1997, 64–73.
  15. [15] ETSI standard document, Speech processing, transmission andquality aspects (STQ), advanced distributed speech recog-nition; front-end feature extraction algorithm; compressionalgorithms, ETSI ES 202 050 v.1.1.4, 2005.
  16. [16] H.L. Kim, D.H. Kim, Y.S. Ryu, and Y.K. Kim, A study onpitch detection using the local peak and valley for Koreanspeech recognition, Proc. IEEE TENCON, 1996, 107–112.
  17. [17] H.G. Hirsch and C. Ehrlicher, Noise estimation techniquesfor robust speech recognition, Proc. IEEE ICASSP, 1, 1995,153–156.
  18. [18] L. Lin, W.H. Holmes, and E. Ambikairajah, Adaptive noiseestimation algorithm for speech enhancement, Electronics Let-ters, 39 (9), 2003, 754–755.
  19. [19] R. Martin, Noise power spectral density estimation based onoptimal smoothing and minimum statistics, IEEE Transactionson Speech and Audio Processing, 9 (5), 2001, 504–512.
  20. [20] I. Cohen and B. Berdugo, Noise estimation by minima con-trolled recursive averaging for robust speech enhancement,IEEE Signal Processing Letters, 9 (1), 2002, 12–15.
  21. [21] C. Shannon, A mathematical theory of communication, BellSystems and Technology Journal, 27, 1948, 379–423, 623–656.
  22. [22] J.L. Shen, J.W. Hung, and L.S. Lee, Robust entropy-based end-point detection for speech recognition in noisy environments,Proc. ICSLP, 1998.
  23. [23] B.F. Wu and K.C. Wang, Robust endpoint detection algorithmbased on the adaptive band-partitioning spectral entropy inadverse environments, IEEE Transactions on Speech and AudioProccessing, 13 (5), 2005, 762–775.
  24. [24] B.-F. Wu and K.-C. Wang, Noise spectrum estimation withentropy-based VAD in non-stationary environments, IEICETransactions on Fundamentals of Electronics, Communicationsand Computer Sciences, E89-A(2), 479–485.
  25. [25] G. Doblinger, Computationally efficient speech enhancementby spectral minima tracking in subbands, Proc. Eurospeech,1995, 1513–1516.
  26. [26] A. Varga and H.J.M. Steeneken, Assessment for automaticspeech recognition: II. NOISEX-92: a database and an experi-ment to study the effect of additive noise on speech recognitionsystems, Speech Communication, 12, 1993, 247–251.

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