This book is intended to give an overview of the major results achieved in the field of natural speech understanding inside ESPRIT Project P. 26, "Advanced Algorithms and Architectures for Speech and Image Processing". The project began as a Pilot Project in the early stage of Phase 1 of the ESPRIT Program launched by the Commission of the European Communities. After one year, in the light of the preliminary results that were obtained, it was confirmed for its 5-year duration. Even though the activities were carried out for both speech and image understand� ing we preferred to focus the treatment of the book on the first area which crystallized mainly around the CSELT team, with the valuable cooperation of AEG, Thomson-CSF, and Politecnico di Torino. Due to the work of the five years of the project, the Consortium was able to develop an actual and complete understanding system that goes from a continuously spoken natural language sentence to its meaning and the consequent access to a database. When we started in 1983 we had some expertise in small-vocabulary syntax-driven connected-word speech recognition using Hidden Markov Models, in written natural lan� guage understanding, and in hardware design mainly based upon bit-slice microprocessors.

Contenuti:

1 Introduction to the Book.- 1.1 Historical Notes.- 1.2 Overview of the Book.- 2 The Recognition Algorithms.- 2.1 Introduction.- 2.2 System Description.- 2.2.1 System Overview.- 2.2.2 Feature Extraction.- 2.2.3 Mel-based Spectral Analysis.- 2.2.4 Vector Quantization.- 2.2.5 The Phonetic Representation.- Phonetic transcription.- Underlying phonetic structure.- Contextual rules.- 2.3 Lexicon Structure.- 2.3.1 Phonetic Segmentation.- Phonetic classification.- Phonetic segmentation.- 2.4 Word Representation.- 2.4.1 Three-Dimensional DP Matching.- Matching costs.- Duration of micro-segments.- Reliability of micro-segments.- 2.4.2 Lexical Access.- Experimental results.- Use of heuristics.- 2.5 Verification Module.- 2.5.1 The Recognition Units.- 2.5.2 Model Estimation.- 2.5.3 Experimental Results.- 2.5.4 Conclusions.- 2.6 Continuous Speech.- 2.6.1 Control Strategies.- Cascade integration.- Full integration.- 2.6.2 Word Hypothesis Normalization.- 2.6.3 Lattice Filters.- 2.6.4 Efficiency Measures.- 2.6.5 Experimental Results.- 2.7 Conclusions.- 3 The Real Time Implementation of the Recognition Stage.- 3.1 Introduction.- 3.2 System Overview.- 3.2.1 Functions Overview.- 3.2.2 Architecture Overview.- 3.2.3 System Control and Synchronization Methods.- 3.2.4 System Run-Time Evolution.- 3.2.5 Details on the Asynchronous Stage Activity.- 3.3 Hardware Details.- 3.3.1 DSP Board Description.- DSP board architecture requirements.- DSP board architecture details.- DSP kernel.- 3.3.2 Acquisition Board Description.- Acquisition board requirements.- Acquisition boards architecture details.- Acquisition functions.- 3.3.3 System Configuration.- 3.4 Firmware Blocks Details.- 3.4.1 Feature Extraction.- Generalities.- DSP1 control details.- DSP1 algorithm details.- 3.4.2 Segmentation and Lexical Access.- 3.4.3 Markov Verifier Firmware.- Generalities.- Verification stage details.- 3.5 Some Details on Other System Functions.- 3.5.1 Program Loading and System Testing.- 3.5.2 Acquisition Firmware Details.- 3.5.3 Parameters Training Environment.- 3.6 System Evaluations.- 3.6.1 General Considerations.- 3.6.2 Single-Step Isolated Words Recognition.- 3.6.3 Two-Step Isolated Words Recognition.- 3.6.4 Single-Step Continuous Speech Recognition.- 3.7 Conclusions.- 4 The Understanding Algorithms.- 4.1 Overview.- 4.1.1 Introduction.- 4.1.2 Some Basic Requirements of a Parser for Speech.- 4.1.3 Knowledge Sources from Dependency Rules and Conceptual Graphs.- 4.1.4 The Importance of Control Strategies.- Two reasons for an effective control strategy.- The role of expectations: Integrating top-down and bottom-up parsing strategies.- Deduction instances and search.- Joining deduction instances.- 4.1.5 Control Strategy and Operators.- 4.1.6 Representing Deduction Instances with Memory Structures.- 4.1.7 Implementation, Development System and Results.- 4.2 Representation of Syntax.- 4.2.1 Introduction.- 4.2.2 Interaction Between Syntactic and Semantic Knowledge.- 4.2.3 Dependency Grammar.- Definitions.- An example.- Relations between dependency grammar and context-free grammar.- Remarks on dependency grammars.- 4.2.4 Morphological Agreement Rules.- Structure of agreement rules.- Definition of agreement rules.- Morphological agreement checks.- Morphological features statically associated to words.- Agreement check modalities.- 4.3 Representation of Semantics.- 4.3.1 Introduction.- 4.3.2 Word Information in the Dictionary.- 4.3.3 Caseframes and Conceptual Graphs.- 4.3.4 The use of Conceptual Graphs.- 4.3.5 Representation of the Utterance Meaning.- 4.4 The Compiler of Conceptual Graphs and Dependency Rules.- 4.4.1 Introduction.- 4.4.2 The Use of Dependency Rules.- 4.4.3 Integrating Conceptual Graphs and Dependency Rules ― the Mapping Knowledge.- 4.4.4 Combining Different Conceptual Graphs.- 4.4.5 A More Complete Example.- 4.5 Parsing ― Conceptual Level.- 4.5.1 Introduction.- 4.5.2 Lexical Component and Model Component.- 4.5.3 Importance of a Score Guided Search.- 4.5.4 Search from the Point of View of the Lexical Component.- Control strategy of the lexical component.- 4.5.5 Relations with the Model Component.- 4.5.6 Relations with some Former Systems.- 4.5.7 The Model Component.- A simplified view: the problem solving paradigm.- The knowledge source partition.- Knowledge sources, facts and goals.- 4.5.8 Deduction Instances.- 4.5.9 Activation: Scores and Quality Factors.- The ACTIVATION operator.- 4.5.10 Control Strategy.- 4.5.11 Optimality and Efficiency.- 4.5.12 The search space and the specialization relation.- 4.5.13 Description of the Operators.- The VERIFY operator.- The SUBGOALING operator.- The PREDICTION operator.- The MERGE operator.- 4.6 Parsing ― Memory Structures.- 4.6.1 Introduction.- 4.6.2 Representing DIs with Memory Structures: Some Problems.- 4.6.3 Canonical Deduction Instances.- 4.6.4 Phrase Hypotheses as Representatives of CDIs.- Phrase hypotheses and AND-OR trees.- Phrase hypotheses and contexts.- 4.6.5 Search Space of CDIs and Links Between PHs.- 4.6.6 The VERIFY Operator.- 4.6.7 The SUBGOALING Operator.- 4.6.8 The PREDICTION Operator.- 4.6.9 The MERGE Operator.- How links are exploited.- 4.7 Parsing ― Dealing with Missing Words.- 4.7.1 Introduction.- 4.7.2 The Problem.- Types of frequently missing short words.- The basic idea.- The approach: the JVERIFY operator.- 4.7.3 How JVERIFY Works.- Search solving.- Default solving.- Integrating search and default solving.- 4.7.4 When to Apply the JVERIFY Operator.- 4.8 Experimental Results.- 4.8.1 General Performance Results.- The coverage of the language model.- Performance results.- 4.8.2 Performance of the Short Word Treatment.- 4.8.3 Optimality and Efficiency.- 4.8.4 Some Specific Problems.- Excessive gaps and overlaps.- Non-optimality.- Jolly words.- 5 Implementation of a Parallel Logic + Functional Language.- 5.1 Overview.- 5.2 Applications.- 5.3 Languages.- 5.3.1 The Language K-LEAF.- 5.3.2 The Language IDEAL.- 5.3.3 Parallel IDEAL and K-LEAF.- 5.4 Models of Computation.- 5.4.1 Compiling IDEAL into K-LEAF.- 5.4.2 Execution of K-LEAF: Flattening and Outermost SLD-Resolution.- 5.4.3 Parallel Outermost Strategy.- 5.5 Language Implementation and Execution.- 5.5.1 The Parallel Virtual Machine for K-LEAF.- 5.5.2 Basic Compilation Scheme for Outermost Strategy.- 5.5.3 The Actual Compilation Scheme.- 5.5.4 C-Emulation of Sequential K-WAM and Benchmarks.- 5.5.5 Execution of OR-parallel K-LEAF.- 5.5.6 Mapping AND-parallelism into OR-parallelism.- 5.5.7 The Actual Parallel Implementation.- 5.6 Hardware Architecture.- 5.6.1 Architectural Overview.- 5.6.2 The Non-Local Communication Network.- 5.6.3 Performance Evaluation.- 5.6.4 The Switching Element.- 5.6.5 The Physical Prototypes.- 5.7 Conclusions.- 5.7.1 Experience with Programming Style.- 5.7.2 Speed-up.- 6 Conclusions and Future Developments.- 6.1 Recognition Algorithms.- 6.2 Real-time Hardware Implementation.- 6.3 Understanding Algorithms.- 6.4 The Role of a Dialogue Manager.

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