Motivation: Increasingly, biological processes are being studied through time series of RNA expression data collected for large numbers of genes. Because common processes may unfold at varying rates in different experiments or individuals, methods are needed that will allow corresponding expression states in different time series to be mapped to one another. Results: We present implementations of time warping algorithms applicable to RNA and protein expression data and demonstrate their application to published yeast RNA expression time series. Programs executing two warping algorithms are described, a simple warping algorithm and an interpolative algorithm, along with programs that generate graphics that visually present alignment information. We show time warping to be superior to simple clustering at mapping corresponding time states. We document the impact of statistical measurement noise and sample size on the quality of time alignments, and present issues related to statistical assessment of alignment quality through alignment scores. We also discuss directions for algorithm improvement including development of multiple time series alignments and possible applications to causality searches and non-temporal processes (`concentration warping'). Availability: Academic implementations of alignment programs genewarp and genewarpi and the graphics generation programs grphwarp and grphwarpi are available as Win32 system DOS box executables on our web site along with documentation on their use. The publicly available data on which they were demonstrated may be found at http://genome-www.stanford.edu/cellcycle/. Postscript files generated by grphwarp and grphwarpi may be directly printed or viewed using GhostView software available at http://www.cs.wisc.edu/#ghost/. Con...

. Initially introduced in the late 1960s and early 1970s, dynamic programming algorithms have become increasingly popular in automatic speech recognition. There are two reasons why this has occurred: First, the dynamic programming strategy can be combined with avery e#cient and practical pruning strategy so that very large search spaces can be handled. Second, the dynamic programming strategy has turned out to be extremely #exible in adapting to new requirements. Examples of such requirements are the lexical tree organization of the pronunciation lexicon and the generation of a word graph instead of the single best sentence. In this paper, we attempt to systematically review the use of dynamic programming search strategies for small#vocabulary and large#vocabulary continuous speech recognition. The following methods are described in detail: search using a linear lexicon, search using a lexical tree, language-model look-ahead and word graph generation. 1 Introduction Search strategie...

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EFFICIENT ALGORITHMS FOR SEQUENCE ANALYSIS WITH CONCAVE AND CONVEX GAP COSTS David A. Eppstein We describe algorithms for two problems in sequence analysis: sequence alignment with gaps (multiple consecutive insertions and deletions treated as a unit) and RNA secondary structure with single loops only. We make the assumption that the gap cost or loop cost is a convex or concave function of the length of the gap or loop, and show how this assumption may be used to develop e#cient algorithms for these problems. We show how the restriction to convex or concave functions may be relaxed, and give algorithms for solving the problems when the cost functions are neither convex nor concave, but can be split into a small number of convex or concave functions. Finally we point out some sparsity in the structure of our sequence analysis problems, and describe how we may take advantage of that sparsity to further speed up our algorithms. CONTENTS 1. Introduction ............................1 ...

This work describes a Finite State Network (FSN), isolated word, speaker dependent, real time Automatic Speech Recognition (ASR) system. FSNs are used to represent grammars belonging to regular language class [ Aho et al., 1974 ] . It is well known that regular languages are powerful enough to generate useful subsets of the natural language. On the other hand, they can be easily integrated into an ASR system [ Bahl et al., 1983 ] [ Rabiner and Juang, Jan 1988 ] embedding grammar constraints to reduce the search space, hence the time spent in recognition, and increase the recognition rate [ Rabiner and Levinson, 1981 ] [ Kai-Fu Lee, April 1988 ] . For isolated word, speaker dependent ASR systems, a template matching recognition algorithm based on Dynamic Time Warping (DTW) [ Velichko and Zagoruyko, 1970 ] [ Vintsyuk, 1968 ] [ Silverman and Morgan, July 1990 ] ensures a high recognition rate and quick development. A single AT&T LSI DSP32C board plugged into an ISA bus has been used as co...

: We consider new algorithms for the solution of many dynamic programming recurrences for sequence comparison and for RNA secondary structure prediction. The techniques upon which the algorithms are based e#ectively exploit the physical constraints of the problem to derive more e#cient methods for sequence analysis. 1. INTRODUCTION In this paper we consider algorithms for two problems in sequence analysis. The first problem is sequence alignment, and the second is the prediction of RNA structure. Although the two problems seem quite di#erent from each other, their solutions share a common structure, which can be expressed as a system of dynamic programming recurrence equations. These equations also can be applied to other problems, including text formatting and data storage optimization. We use a number of well motivated assumptions about the problems in order to provide e#cient algorithms. The primary assumption is that of concavity or convexity. The recurrence relations for bo...

Finding matches, both exact and approximate, between a sequence of symbols A and a pattern P has long been an active area of research in algorithm design. Some of the more well-known byproducts from that research are the diff program and grep family of programs. These problems form a sub-domain of a larger areas of problems called discrete pattern matching which has been developed recently to characterise the wide range of pattern matching problems. This dissertation presents new algorithms for discrete pattern matching over sequences and develops a new sub-domain of problems called discrete pattern matching over interval sets. The problems and algorithms presented here are characterised by pattern matching over interval sets. The problems and al

Abstract—Although automatic word recognition systems have existed for some twenty-five years there is still no suitable standard for evaluating their relative performances. Currently, the merits of two systems cannot be meaningfully compared unless they have been tested with at least the same vocabulary or, preferably, with the same acoustic samples. This paper develops a standard for comparing the performance of different recognizers on arbitrary vocabularies based on a human word recognition model. This standard allows recognition results to be normalized for comparison according to two intuitively meaningful figures of merit: 1) the noise level necessary to achieve comparable human performance and 2) the deviation of the pattern of confusions from human performance. Examples are given of recognizers evaluated in this way, and the role of these performance measures in automatic speech recognition and other related areas is discussed. I.

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To advance research, it is important to identify promising future research directions, especially those that have not been adequately pursued or funded in the past. The working group producing this article was charged to elicit from the human language technology (HLT) community a set of well-considered directions or rich areas for future research that could lead to major paradigm shifts in the field of automatic speech recognition (ASR) and understanding. ASR has been an area of great interest and activity to the signal processing and HLT communities over the past several decades. As a first step, this group reviewed major developments in the field and the circumstances that led to their success and then focused on areas it deemed especially fertile for future research. Part 1 of this article will focus on historically significant developments in the ASR area, including several major research efforts that were guided by different funding agencies, and suggest general areas in which to focus research. Part 2 (to appear in the next issue) will explore in more detail several new avenues holding promise for substantial improvements in ASR performance. These entail cross-disciplinary research and specific approaches to address three-to-five-year grand challenges aimed at stimulating advanced research by dealing with realistic tasks of broad interest.