[u.a.], Monika

Finke, Michael

Kemp, Thomas

Maier, Martin

Rogina, Ivica

Sloboda, Tilo

Waibel, Alex

Woszczyna, Monika

English

[u. a.]

KITopen

DRAFT Tue Jul 25 20:03 1995   ::   icassp96.janus.dviJANUS II | ADVANCESINSPONTANEOUSSPEECH TRANSLATIONczyna M.Finke T.Kemp A.McNair A.Lavie L.Mayeld M.Maier I.RoginaT.Sl oboda A.Waibel P.Zahn T.Zeppenfel dINTERACTI VE SYSTEMS LABORATORI ESat Ca rnegie Me ll on Un i ve r si t y, USAa n d Un i v e r s i t y o f Ka r l s r uhe , Ge r ma nyABSTRACTJANUS I I i s a r e s e a r ch s y s t e m t o d e s i g n a n d t e s t c o mpo -n e n t s o f s p e e c h t o s p e e c h t r a n s l a t i o n s y s t e ms a s we l l a s ar e s e a r c h p r o t o t y p e f o r s u c h a s y s t e m. We wi l l f o c u s o n t woa s p e c t s o f t h e s y s t e m: 1) n e w f e a t u r e s a n d r ef o r ma n c e o f t h e s p e e c ha n d 2) tDRAFT Tue Jul 25 20:03 1995   ::   icassp96.janus.dvi3. RECOGNI TI ON ENGI NETh e r e c o g n i z e r u s e d i n t h e c u r r e n t J ANUS I I p r o t o t y p es y s t e m i s a CDHMMb a s e d r e c o g n i z e r . Th e e x a c t c o n  g u -r a t i o n v a r i e s f r o m t a s t t o t a s k . Fo r t h e l a s t VERBMOBIe v a l u a t i o n o n Ge r ma n s c h e d u l i n g d i a l oc e s s i n g o n a 7 f r a m cDRAFT Tue Jul 25 20:03 1995   ::   icassp96.janus.dviThe two parsers have cl ear strengths and weaknesses.GLR* tri es to match i nput utterances to an i nterl i nguaspeci cati on, so al though words can be ski pped wi th apenal ty, the parser i s l ess robust over di suent i nput. Inputthat i s parsed, though, i s generated i n the target l anguageusi ng syntacti c constrai nts ; thi s means that transl ati onsthrough GLR* are more l i kel y to be compl ete grammati calsentences than those transl ated through PHOENIX, whi chparses and generates onl y at the speech act l evel .GLR* tends to break down when parsi ng l ong utterancesthat are hi ghl y di suent, or that si gni cantl y devi ate f romthe grammar. In many such cases, GLR* succeeds i n pars-i ng onl y a smal l f ragment of the enti re utterance, and i m-portant i nput segments end up bei ng ski pped. PHOENIXi s si gni cantl y better i n anal yzi ng such utterances. BecausePHOENIXi s a chart parser that i s capabl e of ski ppi ng overi nput segments that do not correspond to any top l evel se-manti c concept, i t can recover f romout of domai n segmentsi n the i nput, and \restart" i tsel f on the i n- domai n segmentthat f ol l ows. However, pre- breaki ng i nput to GLR* basedon occurrences of human noi se and parsi ng the shorter sub-utterances separatel y si gni cantl y reduced thi s probl em.Pre- breaki ng benets PHOENIX onl y sl i ghtl y, mai nl y i nbetter resol uti on of t i me express i on attachment ambi gui -t i es . At the current ti me, PHOENIXuses onl y very si mpl edi sambi guati on heuri st i cs , whereas a parse qual i ty mechni smhel ps to deci de between possi bl e parses i n GLR*Computati onal requi rements of GLR*, whi ch i s imented i n l i sp, are f ar greater than those of Pi mpl emented i n C. PHOENIXi s al so much f aster ,i ng 16 ms per parse compared to GLR*' s 1- 2 mi nutBecause the two parsi ng archi tectures perf ormdi erent types of utterances, they may be cway that takes advantage of the strengths5. SPEECHTRANSLATIONRESULTAs the goal of the transl ati on i n JANUS i s to precontent of an utterance, the recogni t i on (SR(MT) and end- to- end qual i ty need to be aof howwel l the meani ng i s preserved.choosen f or eval uati on, good, ok, anTranscription: tuesday mornii f an i mportant semanti c concepduri ng recogni t i on or transltransl ati on i s j udged abad (SR): you saybad (MT): tuesdayi f the meani ng i s psomehow f unny, i tthere i s st i l lok (SR)ok (MT)a 100%cthegg

JANUS II translation of spontaneous conversational speech

https://publikationen.bibliothek.kit.edu/328396/2481

JANUS II translation of spontaneous conversational speech

Abstract

Similar works

Full text

Available Versions

KITopen