Putting Linguistics into Speech Recognition: The Regulus Grammar Compiler Book

	Foreword
1	Introduction	1
1.1	What this book is about	1
1.2	Speech recognition and language models	5
1.3	What Regulus does	13
1.4	Clarissa and MedSLT	15
1.5	Related work	20
1.6	Plan of the book	20
1.7	Summary	21
I	Using Regulus	23
2	Getting started	25
2.1	Getting set up	25
2.2	A toy grammar in GSL	28
2.3	Rewriting Toy0 in Regulus	32
2.4	Regulus configuration files	37
2.5	Using Regulus	39
2.6	Summary	40
3	Simple applications	43
3.1	Introduction	43
3.2	The Regulus Speech Server	44
3.3	A toy dialogue system in Prolog	46
3.4	A toy speech translation system in Prolog	50
3.5	A toy dialogue system in Java	53
3.6	Summary	62
4	Developing grammars	65
4.1	Introduction	65
4.2	Using the Regulus development environment	65
4.3	The Toy1 example grammar	67
4.4	Unification	77
4.5	Macros	81
4.6	Compiling the Toy1 recogniser	85
4.7	Systematic testing of recognisers	87
4.8	Summary	89
5	A spoken dialogue system	93
5.1	Introduction	93
5.2	The Toy1 spoken dialogue system	95
5.3	The input manager	102
5.4	The dialogue manager	104
5.5	The output manager	108
5.6	Integrating dialogue management with recognition	108
5.7	Dealing with ellipsis and corrections	112
5.8	Summary	117
6	A speech translation system	119
6.1	Introduction	119
6.2	Transfer-based systems	120
6.3	Developing translation applications	127
6.4	Translation through interlingua	132
6.5	Translation of ellipsis	134
6.6	Systematic development	138
6.7	Integrating translation with recognition	142
6.8	Summary	145
7	Using grammar specialisation	149
7.1	Overview	149
7.2	Using the general English grammar	150
7.3	The training corpus	154
7.4	Adding lexical entries	156
7.5	General grammar semantics	166
7.6	Multiple top-level specialised grammars	169
7.7	Including lexicon entries directly	169
7.8	Dealing with ambiguity	171
7.9	Making compilation more efficient	171
7.10	Using probabilistic tuning	172
7.11	Summary	173
II	How Regulus Works	175
8	Compiling feature grammars into CFG	177
8.1	Introduction	177
8.2	Exhaustive expansion	178
8.3	Filtering	179
8.4	Efficient filtering of CFGs	182
8.5	Interleaving expansion and filtering	186
8.6	Pre-processing of feature grammars	195
8.7	Transforming the output CFG	199
8.8	Semantics	203
8.9	Summary	203
9	A general English feature grammar for speech	205
9.1	Introduction	205
9.2	What makes speech grammars special	206
9.3	English grammar: basic intuitions	206
9.4	Compositional semantics	209
9.5	Noun phrases	211
9.6	Verb phrases and basic clauses	214
9.7	Adjuncts	228
9.8	Coordination	229
9.9	Feature defaults	230
9.10	Summary	231
10	Grammar specialisation using Explanation Based Learning	233
10.1	Explanation Based Learning	233
10.2	Defining cutting-up criteria	244
10.3	Different kinds of cutting-up criteria	246
10.4	Summary	251
11	Performance of grammar-based recognisers	255
11.1	Introduction	255
11.2	Varying vocabulary size	256
11.3	Varying linguistic coverage	259
11.4	Varying the feature set	261
11.5	Varying the cutting-up criteria	263
11.6	Comparing CFG and PCFG language models	266
11.7	Deriving recognisers from general grammars	267
11.8	Summary	268
12	Comparison of rule-based and robust approaches	271
12.1	Introduction	271
12.2	Methodological issues	272
12.3	Experiments on MedSLT	279
12.4	Experiments on Clarissa	281
12.5	Discussion	282
12.6	Summary	286
13	Summary and future directions	289
13.1	Summary	289
13.2	Future directions	291
Appendix	Online Documentation	293
	References	295
	Index	301