EUROSPEECH '95

We present an approach to linear prediction parameter
estimation and model order selection that utilises Bayesian
inference.
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 analysissynthesis 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 EUROSPEECH1995, 263268.