In recent years, aquestiT of greatieatTV: has been the development of tools and techni8T# tofaci))T#Z the evaluatiT ofdi:ZG9T systems. The latter can be evaluated fromvari(: poi( ofviZK such asrecogni#ZG and understandi # rates,dis,TVV naturalness and robustnessagaist recognissT errors.EvaluatiZ usually requiyT compim -T a large corpus of words and sentences uttered by users, relevant to theappli:VT#Z domai the systemi desimT9for.Thi paper proposes a newtechni9B that makesi possi(9 to reuse such a corpus for theevaluati# and to check the performance of the system whendinTV)G dinTV)G strategiT are used. ThetechniKZ i based on theautomati generatiT of conversati)) between thediT(B(K system, togetherwie anaddiK9T#( didiK9 system user#si8GG8T#()9 wi8 thediT(GZ: system. Thetechni8G has beenappliV to evaluate a di9:K8: system developedi our labusiV twodiT((ZK recogniT#( front-ends and twodiTZ8:( diTZ8:( strategi# to handle user confirmati(KZ The experiVT#( show that the prompt-dependentrecogniepe front-endachi-en better results, but that thi front-endi appropriVG onlyi users lirs thei utterances to those related to the current system prompt. The prompt-i(9VBKTiK front-endachi-en ihi-en results, but enables front-end users to utter anypermi89G utterance at anytiVB iVB9K(T#(ZB of the system prompt. In consequence,thi front-end may allow a more natural and comfortable imfortableT TheexperiBT#( also show that there-promptiV confirmati strategy enhances system performance for both recogniVT# front-ends.