Flexible Mixed-Initiative Dialogue Management using Concept-Level Confidence Measures of Speech Recognizer Output

We i)rcsent a method to r(:aliz(: th:xil)le mix(;(linitiative dialogue, in which the syst(:m can mak(, etti:ctive COlflirmation mad guidmn(:(: using (-oncel)t-leve,1 confidcn('e mcmsur(,s (CMs) derived from st)eech recognizer output in ord(:r to handl(: sl)eech recognition errors. W(: d(:tine two con('et)t-level CMs, which are oil COllt(~,Ilt words and on semantic-attrilmtes, using 10-best outtmts of the Sl)e(:ch r(:cognizt:r and l)arsing with t)hrmse-level grammars. Content-word CM is useflll for s(:lecting 1)]ausible int(:rl)retati(ms. Less contid(:nt illt(:rl)r(:tmtions arc given to confirmation 1)roc(:ss. The strat(:gy iml)roved the interpr(:tmtion accuracy l)y 11.5(/0. Moreover, th(: semanti(:-mttrilmt(: CM ix us(:d to (:stimmtc user's intention and generates syst(mi-initiative guidances (:v(,,n wh(:n suc(-(:sstSfl int(:rl)r(:tmtiol~ is not o|)tain(:(1. 1 I n t r o d u c t i o n In a st)oken dialogu(: system, it fr(:(tuently o(:cm:s that the system incorrectly rccogniz(:s user utterances and the user makes exl)ressions the system has not (~xt)ccted. These prot)lcms arc essentially incvital)le in handling the natural language 1)y comlmters , even if vocal)ulary and grammar of the system are |~lmed. This lack of robustness is one of the reason why spoken dialogue systems have not been widely deployed. In order to realize a rol)ust st)oken dialogue system, it is inevital)le to handle speech recognition errors. To sut)t)ress recognition errors, system-initiative dialogue is eitbctive. But it ca.n 1)e adopted only in a simi)le task. For instance, the form-tilling task can 1)e realized 1)y a simi)le strategy where the system asks a user the slut wdues in a fixed order. In such a systelninitiated intera('tion, the recognizer easily narrows down the vocabulary of the next user's uttcrance, thus the recognition gets easier. ()n the other hand, in more eoniplicat('A task such ms inforination rctriewd, the vocml)ulmry of the llCXI; lltt(2rauco callllot 1)e limited on all occasions, because the user should be abh~ to input the values in various orders based on his i)rel'erence. Therefore, without imposing a rigid teml)late ut)on the user, the system must behav(~ at)t)rol)riately even when sl)ecch recognizer out1)ut contains some errors. Obviously, making confirmal;ion is efl'cctive to mvoid misun(lerstandings caused by slme(:h recognition errors, ttowcver, when contirmmtions are made ]'or every utterance, |;lie di~dogue will l)ccome too redundant mad consequcntly |;rout)lcsomc, for users. Previous works have, shown that confirmation strategy shouM 1)c decided according to the frequency of stretch recognition errors, using mathematicml formula (Niimi and Kolmymshi, 1.996) and using comt)uter-to-comlml;er silnulation (W~tanabe et al., 1!)98). These works assume tixe(t l )erfof mance (averaged speech recognition accuracy) in whole (lialogue with any speakers. For flexible dialogue management, howeve, r the confirmation strategy luust 1)e dynamically changc, d bmsed on the individual utterances. For instmncc, we human make contirmation only when we arc not coat|dent. Similarly, confidence, incasures (CMs) of every speech recognition output should be modeled as a criterion to control dialogue management. CMs have been calculated in previous works using transcripts and various knowledge sources (Litman et al., 1999) (Pao et, al., 1998). For more tlexible interaction, it, ix desirable that CMs are detined on each word rather than whole sentence, because the systeln can handle only unreliable portions of an ut terance instead of accepting/reject ing whole sentence.