Real Field Deployment of a Smart Fiber Optic Surveillance System for Pipeline Integrity Threat Detection: Architectural Issues and Blind Field Test Results

This paper presents an on-line augmented surveillance system that aims to real time monitoring of activities along a pipeline. The system is deployed in a fully realistic scenario and exposed to real activities carried out in unknown places at unknown times within a given test time interval (socalled blind field tests). We describe the system architecture that includes specific modules to deal with the fact that continuous on-line monitoring needs to be carried out, while addressing the need of limiting the false alarms at reasonable rates. To the best or our knowledge, this is the first published work in which a pipeline integrity threat detection system is deployed in a realistic scenario (using a fiber optic along an active gas pipeline) and is thoroughly and objectively evaluated in realistic blind conditions. The system integrates two operation modes: The machine+activity identification mode identifies the machine that is carrying out a certain activity along the pipeline, and the threat detection mode directly identifies if the activity along the pipeline is a threat or not. The blind field tests are carried out in two different pipeline sections: The first section corresponds to the case where the sensor is close to the sensed area, while the second one places the sensed area about 35 km far from the sensor. Results of the machine+activity identification mode showed an average machine+activity classification rate of 46:6%. For the threat detection mode, 8 out of 10 threats were correctly detected, with only 1 false alarm appearing in a 55:5-hour sensed period.European CommissionMinisterio de Economía y CompetitividadComunidad de Madri

Automatic speech segmentation : why and what segments ?

I present and discuss the SAPHO (Segmentation by Acoustico-Phonetic knowledge) model implemented in Awk language under the Unix system on a MASSCOMP computer. The system is devised as a speaker independent ASS (automatic speech segmentation), by a previous recognition of the phonetic articulation manner. In ail the ASR systems the phonetic knowledge is at least implicitely used . Il has to be explicitely referred to . The phonemic units cannot be directly built from the acoustic signal and are not available at the output of SAPHO . According to the Level Building procedure SAPHO supplies a hierarchized set of acoustic properties and segments, and phonetic properties and segments which fit the phonetic parsing of the acoustic wave . The amenability of this system is entailed by ils modularity which allows a possible further architecture as distributed tasks.The processors are concieved either as data driven with numeric computalion or as expectation driven activities with symbolic computation . The recursivity in the acoustic and the phonetic supervisors at each step of the parsing ensures the likelihood of the décisions . The suitability and the reliability of SAPHO are corroborated by the accuracy of the results .Nous présentons et discutons le modèle SAPHO (segmentation par les connaissances acoustico-phonétiques) mis en ouvre en langage AWK sous UNIX, sur une station de travail Masscomp . Ce système est conçu comme une procédure de segmentation indépendante du locuteur fondée sur une reconnaissance préalable du mode d'articulation phonétique . Dans la plupart des modèles RAP, les connaissances phonétiques sont toujours utilisées, au moins de façon implicite . Elles doivent l'être de façon explicite. Les unités phonémiques ne peuvent pas être directement construites à partir du signal acoustique ; elles ne sont pas encore disponibles à la sortie de SAPHO . Suivant le modèle de Construction de Niveaux (Level Building), SAPHO fournit un ensemble hiérarchisé de propriétés et de segments acoustiques, de propriétés et de segments phonétiques congruents avec les unités phonétiques et leur structure interne . La souplesse de ce système est assurée par sa modularité . La fiabilité de SAPHO est corroborée par l'exactitude des résultats

Linguistic constraints for large vocabulary speech recognition.

by Roger H.Y. Leung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 79-84).Abstracts in English and Chinese.ABSTRACT --- p.IKeywords: --- p.IACKNOWLEDGEMENTS --- p.IIITABLE OF CONTENTS: --- p.IVTable of Figures: --- p.VITable of Tables: --- p.VIIChapter CHAPTER 1 --- INTRODUCTION --- p.1Chapter 1.1 --- Languages in the World --- p.2Chapter 1.2 --- Problems of Chinese Speech Recognition --- p.3Chapter 1.2.1 --- Unlimited word size: --- p.3Chapter 1.2.2 --- Too many Homophones: --- p.3Chapter 1.2.3 --- Difference between spoken and written Chinese: --- p.3Chapter 1.2.4 --- Word Segmentation Problem: --- p.4Chapter 1.3 --- Different types of knowledge --- p.5Chapter 1.4 --- Chapter Conclusion --- p.6Chapter CHAPTER 2 --- FOUNDATIONS --- p.7Chapter 2.1 --- Chinese Phonology and Language Properties --- p.7Chapter 2.1.1 --- Basic Syllable Structure --- p.7Chapter 2.2 --- Acoustic Models --- p.9Chapter 2.2.1 --- Acoustic Unit --- p.9Chapter 2.2.2 --- Hidden Markov Model (HMM) --- p.9Chapter 2.3 --- Search Algorithm --- p.11Chapter 2.4 --- Statistical Language Models --- p.12Chapter 2.4.1 --- Context-Independent Language Model --- p.12Chapter 2.4.2 --- Word-Pair Language Model --- p.13Chapter 2.4.3 --- N-gram Language Model --- p.13Chapter 2.4.4 --- Backoff n-gram --- p.14Chapter 2.5 --- Smoothing for Language Model --- p.16Chapter CHAPTER 3 --- LEXICAL ACCESS --- p.18Chapter 3.1 --- Introduction --- p.18Chapter 3.2 --- Motivation： Phonological and lexical constraints --- p.20Chapter 3.3 --- Broad Classes Representation --- p.22Chapter 3.4 --- Broad Classes Statistic Measures --- p.25Chapter 3.5 --- Broad Classes Frequency Normalization --- p.26Chapter 3.6 --- Broad Classes Analysis --- p.27Chapter 3.7 --- Isolated Word Speech Recognizer using Broad Classes --- p.33Chapter 3.8 --- Chapter Conclusion --- p.34Chapter CHAPTER 4 --- CHARACTER AND WORD LANGUAGE MODEL --- p.35Chapter 4.1 --- Introduction --- p.35Chapter 4.2 --- Motivation --- p.36Chapter 4.2.1 --- Perplexity --- p.36Chapter 4.3 --- Call Home Mandarin corpus --- p.38Chapter 4.3.1 --- Acoustic Data --- p.38Chapter 4.3.2 --- Transcription Texts --- p.39Chapter 4.4 --- Methodology: Building Language Model --- p.41Chapter 4.5 --- Character Level Language Model --- p.45Chapter 4.6 --- Word Level Language Model --- p.48Chapter 4.7 --- Comparison of Character level and Word level Language Model --- p.50Chapter 4.8 --- Interpolated Language Model --- p.54Chapter 4.8.1 --- Methodology --- p.54Chapter 4.8.2 --- Experiment Results --- p.55Chapter 4.9 --- Chapter Conclusion --- p.56Chapter CHAPTER 5 --- N-GRAM SMOOTHING --- p.57Chapter 5.1 --- Introduction --- p.57Chapter 5.2 --- Motivation --- p.58Chapter 5.3 --- Mathematical Representation --- p.59Chapter 5.4 --- Methodology: Smoothing techniques --- p.61Chapter 5.4.1 --- Add-one Smoothing --- p.62Chapter 5.4.2 --- Witten-Bell Discounting --- p.64Chapter 5.4.3 --- Good Turing Discounting --- p.66Chapter 5.4.4 --- Absolute and Linear Discounting --- p.68Chapter 5.5 --- Comparison of Different Discount Methods --- p.70Chapter 5.6 --- Continuous Word Speech Recognizer --- p.71Chapter 5.6.1 --- Experiment Setup --- p.71Chapter 5.6.2 --- Experiment Results: --- p.72Chapter 5.7 --- Chapter Conclusion --- p.74Chapter CHAPTER 6 --- SUMMARY AND CONCLUSIONS --- p.75Chapter 6.1 --- Summary --- p.75Chapter 6.2 --- Further Work --- p.77Chapter 6.3 --- Conclusion --- p.78REFERENCE --- p.7

Lexical access to large vocabularies for speech recognition

A large vocabulary isolated word recognition system based on the hypothesize-and-test paradigm is described. The system has been, however, devised as a word hypothesizer for a continuous speech understanding system able to answer to queries put to a geographical database. Word preselection is achieved by segmenting and classifying the input signal in terms of broad phonetic classes. Due to low redundancy of this phonetic code for lexical access, to achieve high performance, a lattice of phonetic segments is generated, rather than a single sequence of hypotheses. It can be organized as a graph, and word hypothesization is obtained by matching this graph against the models of all vocabulary words. A word model is itself a phonetic representation made in terms of a graph accounting for deletion, substitution, and insertion errors. A modified Dynamic Programming (DP) matching procedure gives an efficient solution to this graph-to-graph matching problem. Hidden Markov Models (HMM's) of subword units are used as a more detailed knowledge in the verification step. The word candidates generated by the previous step are represented as sequences of diphone-like subword units, and the Viterbi algorithm is used for evaluating their likelihood. To reduce storage and computational costs, lexical knowledge is organized in a tree structure where the initial common subsequences of word descriptions are shared, and a beam-search strategy carries on the most promising paths only. The results show that a complexity reduction of about 73 percent can be achieved by using the two pass approach with respect to the direct approach, while the recognition accuracy remains comparable