Results 1 
8 of
8
Quantization
 IEEE TRANS. INFORM. THEORY
, 1998
"... The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modula ..."
Abstract

Cited by 639 (11 self)
 Add to MetaCart
The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modulation systems, especially in the 1948 paper of Oliver, Pierce, and Shannon. Also in 1948, Bennett published the first highresolution analysis of quantization and an exact analysis of quantization noise for Gaussian processes, and Shannon published the beginnings of rate distortion theory, which would provide a theory for quantization as analogtodigital conversion and as data compression. Beginning with these three papers of fifty years ago, we trace the history of quantization from its origins through this decade, and we survey the fundamentals of the theory and many of the popular and promising techniques for quantization.
A Fast Nearest Neighbor Algorithm Based on a Principal Axis Search Tree
, 2001
"... A new fast nearest neighbor algorithm is described that uses principal component analysis to build an efficient search tree. At each node in the tree, the data set is partitioned along the direction of maximum variance. The search algorithm efficiently uses a depthfirstsearch and a new elimination ..."
Abstract

Cited by 26 (0 self)
 Add to MetaCart
A new fast nearest neighbor algorithm is described that uses principal component analysis to build an efficient search tree. At each node in the tree, the data set is partitioned along the direction of maximum variance. The search algorithm efficiently uses a depthfirstsearch and a new elimination criterion. The new algorithm was compared to sixteen other fast nearest neighbor algorithms on three types of common benchmark data sets including problems from time series prediction and image vector quantization. This comparative study illustrates the strengths and weaknesses of all of the leading algorithms. The new algorithm performed very well on all of the data sets and was consistently ranked among the top three algorithms.
StateBased Gaussian Selection In Large Vocabulary Continuous Speech Recognition Using HMMs
, 1997
"... This paper investigates the use of Gaussian Selection (GS) to increase the speed of a large vocabulary speech recognition system. Typically 3070% of the computational time of a HMMbased speech recogniser is spent calculating probabilities. The aim of GS is to reduce this load by dividing the acoust ..."
Abstract

Cited by 23 (2 self)
 Add to MetaCart
This paper investigates the use of Gaussian Selection (GS) to increase the speed of a large vocabulary speech recognition system. Typically 3070% of the computational time of a HMMbased speech recogniser is spent calculating probabilities. The aim of GS is to reduce this load by dividing the acoustic space into a set of clusters and associating a "shortlist" of Gaussians with each of these clusters. Any Gaussian not in the shortlist is simply approximated. This paper examines new techniques for obtaining "good" shortlists. All the new schemes make use of state information, specifically which state each of the components belongs to. In this way a maximum number of components per state may be specified, hence reducing the size of the shortlist. The first technique introduced is a simple extension of the standard GS one, which uses this state information. Then, more complex schemes based on maximising the likelihood of the training data are proposed. These new approaches are compared...
Use Of Gaussian Selection In Large Vocabulary Continuous Speech Recognition Using HMMs
, 1996
"... This paper investigates the use of Gaussian Selection (GS) to reduce the state likelihood computation in HMMbased systems. These likelihood calculations contribute significantly (30 to 70%) to the computational load. Previously, it has been reported that when GS is used on large systems the recogni ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
This paper investigates the use of Gaussian Selection (GS) to reduce the state likelihood computation in HMMbased systems. These likelihood calculations contribute significantly (30 to 70%) to the computational load. Previously, it has been reported that when GS is used on large systems the recognition accuracy tends to degrade above a \Theta3 reduction in likelihood computation. To explain this degradation, this paper investigates the tradeoffs necessary between achieving good state likelihoods and low computation. In addition, the problem of unseen states in a cluster is examined. It is shown that further improvements are possible. For example, using a different assignment measure, with a constraint on the number of components per state per cluster, enabled the recognition accuracy on a 5k speakerindependent task to be maintained up to a \Theta5 reduction in likelihood computation. 1. INTRODUCTION In recent years, high accuracy large vocabulary continuous speech recognition sys...
Fast NearestNeighbor Search Algorithms Based on ApproximationElimination Search
 In Proceedings of the ACMSIAM Symposium on Discrete Algorithms
, 2000
"... In this paper, we provide an overview of fast nearestneighbor search algorithms based on an &approxima tion}elimination' framework under a class of elimination rules, namely, partial distance elimination, hypercube elimination and absoluteerrorinequality elimination derived from approximations o ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
In this paper, we provide an overview of fast nearestneighbor search algorithms based on an &approxima tion}elimination' framework under a class of elimination rules, namely, partial distance elimination, hypercube elimination and absoluteerrorinequality elimination derived from approximations of Euclidean distance. Previous algorithms based on these elimination rules are reviewed in the context of approximation}elimination search. The main emphasis in this paper is a comparative study of these elimination constraints with reference to their approximation}elimination e$ciency set within di!erent approximation schemes. # 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Fast FullSearch Equivalent NearestNeighbour Search Algorithms
, 1999
"... A fundamental activity common to many image processing, pattern classification, and clustering algorithms involves searching a set of n, kdimensional data for the one which is nearest to a given target item with respect to a distance function. Our goal is to find fast search algorithms which are fu ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
A fundamental activity common to many image processing, pattern classification, and clustering algorithms involves searching a set of n, kdimensional data for the one which is nearest to a given target item with respect to a distance function. Our goal is to find fast search algorithms which are fullsearch equivalentthat is, the resulting match is as good as what we could obtain if we were to search the set exhaustively. We propose a framework made up of three components, namely (i) a technique for obtaining a good initial match, (ii) an inexpensive method for determining whether the current match is a fullsearch equivalent match, and (iii) an effective technique for improving the current match. Our approach is to consider good solutions for each component in order to find an algorithm which balances the overall complexity of the search. We also propose a technique for hierarchical ordering and cluster elimination using a minimal cost spanning tree. Our experiments on vector quantisation coding of images show that the framework and techniques we proposed can be used to construct suitable algorithms for most of our data sets which require fullsearch equivalent matches at an average arithmetic cost of less than O(k log n) while using only O(n) space.
In Search of the Optimal Searching Sequence for VQ Encoding
, 1995
"... The codeword searching sequence is sometimes very vital to the efficiency of a VQ encoding algorithm. In this paper, we evaluate some necessary criteria for the derivation of an optimal searching sequence and derive the optimal searching sequence based on such criteria. Keywords I. Introduction ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
The codeword searching sequence is sometimes very vital to the efficiency of a VQ encoding algorithm. In this paper, we evaluate some necessary criteria for the derivation of an optimal searching sequence and derive the optimal searching sequence based on such criteria. Keywords I. Introduction T WO common strategies have been used to reduce the complexity inherent in Vector Quantization (VQ) encoding algorithms. One resorts to simpler but suboptimal variants and sacrifices quality such as the tree searched VQ[1]. The other remains with the original VQ and devises fast algorithms such as the partial distance search (PDS). This second category of algorithms is more flexible since they are codebookindependent[28], but the searching sequence of the codewords is very vital to the efficiency of the algorithms. Consider the case that one has to represent a given Ddimensional input vector ~x = (x 1 ; x 2 :::x D ) with a particular codeword selected from a codebook containing N codew...
Use Of Gaussian Selection In Large Vocabulary Continuous Speech Recognition Using HMMs
, 1996
"... This paper investigates the use of Gaussian Selection (GS) to reduce the state likelihood computation in HMMbased systems. These likelihood calculations contribute significantly (30 to 70%) to the computational load. Previously, it has been reported that when GS is used on large systems the recogni ..."
Abstract
 Add to MetaCart
This paper investigates the use of Gaussian Selection (GS) to reduce the state likelihood computation in HMMbased systems. These likelihood calculations contribute significantly (30 to 70%) to the computational load. Previously, it has been reported that when GS is used on large systems the recognition accuracy tends to degrade above a #3 reduction in likelihood computation. To explain this degradation, this paper investigates the tradeoffs necessary between achieving good state likelihoods and low computation. In addition, the problem of unseen states in a cluster is examined. It is shown that further improvements are possible. For example, using a different assignment measure, with a constraint on the number of components per state per cluster, enabled the recognition accuracy on a 5k speakerindependent task to be maintained up to a #5 reduction in likelihood computation.