This paper investigates the impact of Constraint Dependency Grammars (CDG) on the accuracy of an integrated speech recognition and CDG parsing system. We compare a conventional CDG with CDGs that are induced from annotated sentences and template-expanded sentences. The grammars are evaluated on parsing speed, precision/coverage, and improvement of word and sentence accuracy of the integrated system. Sentence-derived CDGs significantly improve recognition accuracy over the conventional CDG but are less general. Expanding the sentences with templates provides us with a mechanism for increasing the coverage of the grammar with only minor reductions in recognition accuracy.

ROLE VALUES A Thesis Submitted to the Faculty Purdue University by Christopher M. White In Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy May 2000 - ii - To my loving wife Margit.

Large vocabulary speech recognition applications can benefit from an efficient data structure for representing large numbers of acoustic hypotheses compactly. Word graphs or lattices generated by acoustic recognition engines are generally not compact and must be post-processed to keep lattice sizes small; however, algorithms designed for this task need to reduce the size of the lattice without either eliminating hypotheses or distorting their relative acoustic probabilities. In this paper, we will discuss the relevant criteria for measuring graph size, compare the advantages of two different structures for graphs, and introduce a new data structure and compression algorithm which give additional graph compression and maintain exact hypothesis path scores by storing probability information on both nodes and arcs within the graph. 1. INTRODUCTION Many recognition systems use word lattices or graphs as mechanisms for representing sentence hypotheses and interfacing with additional knowle...

Larr vocabular y continuous speech reech ition can benefitfre an e#cient data strR turfor rrR/sentingalarE number of acoustic hypotheses compactly. Wor gr1:1 or lattices have been chosen as such an e#cientinter face between acousticroust ition engines and subsequent languageprguag ing modules. This paper firR investigates the e#ect ofprEI/-- dur ing acoustic decoding on the quality ofwor lattices and shows that by combiningdi#erEE pre ing options (at the model level and wor level), we can obtain wor lattices withcompar bleaccurE/ to theorRE/ al lattices and a manageable size. In orer to use the wor lattices as the inputfor a post-prt-RI ing language module, they shouldprx--:/1 thetar/E hypotheses andtheir scor while being as small as possible. In this paper weintr oduce awor grC comprmpR/-- algor thm that significantlyrnt ces the number ofwor-- in thegrRxEE alrRx---- entation without eliminatingutter ance hypothesesor distortRI their acousticscort . Wecompar this wor grR comprCx/)R algor thm withsever lother latticesize-rRI cing appr aches and demon strnR thereRx1C-- strx gth of the new wor gr1/ comprw sionalgor:I+ for decr: ing the number ofworC in thereR/) entation. ExperR entsar conductedacrRI corRI/ and vocabular sizes todeterE/R the consistency of theprR/--) and comprC sionrnRIIC) # 2003 Elsevier Science Ltd. AllrlRI srEIE ved. 1.I5k4 Wor latticesar often chosen as theinter/C1 between an acousticrusticRx-- and a subsequent prubsequ using amor complex language model (LM)or mor specific acoustic model because of www.elsevierw.elsevi te/csl COMPUTER SPEECH AND LANGUAGE * Corr)R)R)Rr author Tel.: +1-765-494-3652; fax: +1-765-494-3371. E-mailaddr9(--)b harRxC/1:Rwxxx/Rrx+ yangl@ecn.purxxx/Rr (M.P.Har.RIC mike.johnson@marrx+Rwxx (M.T. Johnson),lhj@ecn.pur)xRwEE...