The DORIS project aims to develop a spoken dialogue module for an autonomous robotic agent. This paper examines the techniques used by Scusi?, the speech interpretation component of DORIS, to postulate and assess hypotheses regarding the meaning of a spoken utterance. The results of our evaluation are encouraging, yielding good interpretation performance for utterances of different types and lengths.

This paper examines orthonormal regression and wavelet denoising within the Minimum Message Length (MML) framework. A criterion for hard thresholding that naturally incorporates parameter shrinkage is derived from a hierarchical Bayes approach. Both parameters and hyperparameters are jointly estimated from the data directly by minimisation of a two-part message length, and the threshold implied by the new criterion is shown to have good asymptotic optimality properties with respect to zero-one loss under certain conditions. Empirical comparisons made against similar criteria derived from the Minimum Description Length principle demonstrate that the MML procedure is competitive in terms of squared-error loss.

This paper examines MMLD-based approximations for the inference of two univariate probability densities: the geometric distribution, parameterised in terms of a mean parameter, and the Poisson distribution. The focus is on both parameter estimation and hypothesis testing properties of the approximation. The new parameter estimators are compared to the MML87 estimators in terms of bias, squared error risk and KL divergence risk. Empirical experiments demonstrate that the MMLD parameter estimates are more biased, and feature higher squared error risk than the corresponding MML87 estimators. In contrast, the two criteria are virtually indistinguishable in the hypothesis testing experiment.

This paper presents an efficient and general solution to the linear regression problem using the Minimum Message Length (MML) principle. Inference in an MML framework involves optimising a two-part costing function that describes the trade-off between model complexity and model capability. The MML criterion is integrated into the orthogonal least squares algorithm (MML-OLS) to improve both speed and numerical stability. This allows for the message length to be iteratively updated with the selection of each new regressor, and for potentially problematic regressors to be rejected. The MMLOLS algorithm is subsequently applied to function approximation with univariate polynomials. Empirical results demonstrate superior performance in terms of mean squared prediction error in comparison to several well-known benchmark criteria.