Hamiltonian constraints feature in the canonical formulation of general relativity. Unlike typical constraints they cannot be associated with a reduction procedure leading to a non-trivial reduced phase space and this means the physical interpretation of their quantum analogues is ambiguous. In particular, can we assume that “quantisation commutes with reduction” and treat the promotion of thes...

We present a simple derivation of the WKB quantisation condition using the quantum Hamilton-Jacobi formalism and propose an exact quantisation condition within this formalism for integrable models in higher dimensions.

We propose a unified framework for representing and processing images using a feature space related to local similarity. We choose the multiscale and versatile local jet feature space to represent the visual data. This feature space may be reduced by vector quantisation and/or be represented by data structures enabling efficient nearest neighbours search (e.g. kd-trees). We show the interest of...

This paper presents a speaker independent isolated word recogniser, which combines the product codebook vector quantisation principle with the discrete hidden Markov modeHing (HMM), so that each frame in the unknown test word ( or training word) is described by two symbols, the linear predictive coding (LPC) shape and gain. The recogniser (both training and testing) has been evaluated on a 12 w...

A discussion is given of the quantisation of a physical system with finite degrees of freedom subject to a Hamiltonian constraint by treating time as a constrained classical variable interacting with an unconstrained quantum state. This leads to a quantisation scheme that yields a Schrödinger-type equation which is in general nonlinear in evolution. Nevertheless it is compatible with the probab...

In vector quantisation (VQ) based speaker recognition, the minimum overall average distortion rule is used as a criterion to assign a given sequence of acoustic vectors to a speaker model known as a codebook. An alternative decision rule based on fuzzy c-means clustering is proposed in this paper. A set of membership functions associated with vectors for codebooks are defined as discriminant fu...

An investigation into the relative speaker verification performance of various types of vector quantisation (VQ) and dynamic time warping (DTW) classifiers is presented. The study covers a number of algorithmic issues involved in the above classifiers, and examines the effects of these on the verification accuracy. The experiments are based on the use of a subset from the Brent (telephone quali...

Vector Quantisation (VQ) has been shown to be robust in speaker recognition systems which require a small amount of training data. However the conventional VQ-based method only uses distortion measurements and discards the sequence of quantised codewords. In this paper we propose a method which extends the VQ distortion method by combining it with the likelihood of the sequence of VQ indices ag...

In speaker recognition, the maximum likelihood (ML) rule is used as a criterion to assign a given sequence of acoustic vectors to the maximum likelihood speaker model. However, this rule is not flexible in some cases. An alternative decision rule, the maximum average normalised likelihood (MANL), is proposed in this paper. The theoretical analysis and the experimental results show that the MANL...