Non-linear Time Series Analysis: This section contains papers which discuss various modeling techniques for a non-linear time series.

• General techniques:
• J. Farmer and J. Sidorowich, "Predicting chaotic time series," Physical Review Letters, vol. 59, no. 8, pp. 845-848, August 1987 [pdf].
  
  
• H. Abarbanel, "Prediction in chaotic nonlinear systems: Methods for time series with broadband Fourier spectra," Physical Review A, The American Physical Society, vol. 41, no. 4, pp. 1782-1807, February 1990 [pdf].
  
  
• M. Casdagli, "Nonlinear Prediction of Chaotic Time Series," Physica D. Nonlinear Phenomena,  vol. 35, no. 3, pp. 335-356, May 1989.
  
  
• Discriminative techniques:
• S. Mukherjee, E. Osuna, F. Girosi, "Nonlinear Prediction of Chaotic Time Series using  Support Vector Machines," Proceedings of IEEE Workshop on Neural Networks for Signal Processing VII, NNSP'97, Amelia Island, Fl, USA, pp. 511-519, 1997.
  
  

General Nonlinear Statistics: This section contains papers which are drawn from the general mathematics and statistics literature, which discuss the theory and background of various statistical methods explored in this project.

• Particle Filtering:
• S. Haykin and E. Moulines, "From Kalman to Particle Filters," IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, Pennsylvania, USA, March 2005 [pdf].
  
  
• M.W. Andrews, "Learning And Inference In Nonlinear State-Space Models," Gatsby Unit for Computational Neuroscience, University College, London, U.K., December 2004 (in preparation) [pdf].


• P. Djuric, J. Kotecha, J. Zhang, Y. Huang, T. Ghirmai, M. Bugallo, and J. Miguez, "Particle Filtering," IEEE Magazine on Signal Processing, vol. 20, no. 5, pp. 19-38, September 2003.
  
  
• N. Arulampalam, S. Maskell, N. Gordan, and T. Clapp, "Tutorial On Particle Filters For Online Nonlinear/ Non-Gaussian Bayesian Tracking," IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188, February 2002 [pdf].
  
  
• R. van der Merve, N. de Freitas, A. Doucet, and E. Wan, "The Unscented Particle Filter," Technical Report CUED/F-INFENG/TR 380, Cambridge University Engineering Department, Cambridge University, U.K., August 2000 [ps.gz].
  
  

• Kalman Filtering:
• G. Welch and G. Bishop, "An Introduction to the Kalman Filter," Technical Report TR 95-041, Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA, April 2004 [pdf].
  
  
• Simon J. Julier, Jeffrey K. Ulhmann, "A New Extension of the Kalman Filter to Nonlinear Systems", Int. Symp. Aerospace/Defense Sensing, Simul. and Controls, Orlando, FL, 1997 [pdf]
  
  

Applications to Speech Recognition and Signal Processing: This section contains papers which are related to applications of nonlinear statistical methods in speech and signal processing.

• Particle Filtering:
• S. Gannot, and M. Moonen, "On The Application Of The Unscented Kalman Filter To Speech Processing," International Workshop on Acoustic Echo and Noise, Kyoto, Japan, pp. 27-30, September 2003 [pdf].
  
  
• J.P. Norton, and G.V. Veres, "Improvement Of The Particle Filter By Better Choice Of The Predicted Sample Set," presented at the 15th IFAC Triennial World Congress, Barcelona, Spain, July 2002 [pdf].
  
  
• J. Vermaak, C. Andrieu, A. Doucet, and S.J. Godsill, "Particle Methods For Bayesian Modeling And Enhancement Of Speech Signals," IEEE Transaction on Speech and Audio Processing, vol. 10, no. 3, pp. 173-185, March 2002 [pdf].
  
  

State of Art ASR Systems: This section contains papers which are related to State of Art Automatic Speech Recognition Systems.

• Discriminative Methods:
• D. Povey, M.J.F. Gales, D.Y. Kim and P.C. Woodland, "MMI-MAP and MPE-MAP for Acoustic Model Adaption," Proceedings of Euroscpeech 2003, Geneva, Switzerland, pp. 1981-1984, September 2003 [ ps.gz, pdf].
  
  
• D. Povey, P.C. Woodland, and M.J.F. Gales, "Discriminative map for acoustic model adaption," IEEE International Conference on Acoustics, Speech, and Signal Processing, Hong-Kong, vol. 1, pp I-312-315, April 2003 [ ps.gz ].
  
  
• D. Povey, and P.C. Woodland, "Minimum Phone Error and I-Smoothing for Improved Discriminative Training," IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, Florida, USA, vol. 1, pp. I-105-108, May 2002.
  
  

Useful Resources: This section contains useful external resources such as links to software, applets, Matlab links etc.

• Parzen Windows Demo