We present an approach to linear prediction parameter
estimation and model order selection that utilises Bayesian
The addition of a penalty term, or regulariser, to the conventional linear prediction data error term prior to minimising it facilitates the estimation of the maximum a posteriori parameters. A direct equivalence can be drawn between the type of regulariser used and the prior assumptions regarding the solution to a linear prediction problem. Mackay's Bayesian Evidence framework is used for the estimation of linear prediction parameters that reflect the role that prior assumptions play during the analysis of a speech segment.
Quadratic regularisers are utilised to parametrise speech signals and the results are demonstrated with formant tracking and analysis-synthesis examples.
Bibliographic reference. Saleh, G. M. K. / Niranjan, M. / Fitzgerald, W. J. (1995): "The use of maximum a posteriori parameters in linear prediction of speech", In EUROSPEECH-1995, 263-268.