From the theory of Bayesian pattern recognition it is well known that the maximum a posteriori decision criterion yields a recogniser with the minimum probability of assigning the incorrect label to a pattern, if the correct probability distributions are used. This MAP criterion is also routinely employed in automatic speech recognition system. The problem addressed in this thesis is the fact that in a continuous speech recognition task the MAP criterion only guarantees to minimise the sentence error rate, but for many application the by far more relevant error metric is the word error rate.

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...

This paper describes the techniques we explored to improve the translation of news text in the German-English and Hungarian-English tracks of the WMT09 shared translation task. Beginning with a convention hierarchical phrase-based system, we found benefits for using word segmentation lattices as input, explicit generation of beginning and end of sentence markers, minimum Bayes risk decoding, and incorporation of a feature scoring the alignment of function words in the hypothesized translation. We also explored the use of monolingual paraphrases to improve coverage, as well as co-training to improve the quality of the segmentation lattices used, but these did not lead to improvements. 1