Speech recognition performance is severely affected when the lexical, syntactic, or semantic characteristics of the discourse in the training and recognition tasks differ. The aim of language model adaptation is to exploit specific, albeit limited, knowledge about the recognition task to compensate for this mismatch. More generally, an adaptive language model seeks to maintain an adequate representation of the current task domain under changing conditions involving potential variations in vocabulary, syntax, content, and style. This paper presents an overview of the major approaches proposed to address this issue, and offers some perspectives regarding their comparative merits and associated tradeoffs. Ó 2003 Elsevier B.V. All rights reserved. 1.

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.

We introduce a hierarchical statistical language model, represented as a collection of local models plus a general sentence model. We provide an example that mixes a trigram general model and a PFSA local model for the class of decimal numbers, described in terms of sub-word units (graphemes). This model practically extends the vocabulary of the overall model to an infinite size, but still has better performance compared to a word-based model. Using in-domain language model adaptation experiments, we show that local models can encode enough linguistic information, if well trained, that they may be ported to new language models without re-estimation.

This paper presents a novel technique that allows testing spoken dialogue systems by means of an automatic generation of conversations. The technique permits to easily test spoken dialogue systems under a variety of lab-simulated conditions, as it is easy to vary or change the utterance corpus used to check the performance of the system. The technique is based on the use of a module called user simulator whose purpose is to behave as real users when they interact with dialogue systems. The behaviour of the simulator is decided by means of diverse scenarios that represent the goals of the users. The simulator aim is to achieve the goals set in the scenarios during the interaction with the dialogue system. We have applied the technique to test a dialogue system developed in our lab. The test has been carried out considering different levels of white and babble noise as well as a VTS noise compensation technique. The results prove that the dialogue system performance is worse under the babble noise conditions. The VTS technique has been effective when dealing with noisy utterances and has lead to better experimental results, particularly for the white noise. The technique has permitted to detect problems in the dialogue strategies employed to handle confirmation turns and recognition errors, suggesting that these strategies must be improved. q 2002 Elsevier Science B.V. All rights reserved.

This paper describes a method for building statistical Language Models (LMs) dedicated to specific dialogue situations. The architecture of the speech recognition system proposed uses several LMs. The first stage of this system, consists of producing a word-lattice from a given sentence uttered by a speaker. A general LM calculates a sentence-hypothesis. Then, in a second stage, the system chooses a specialized LM according to the word-lattice and the previous hypothesis. Another decoding process is performed using this specialized LM in order to produce a new sentence- hypothesis. Finally, a decision-module processes these two hypotheses in order to assign three confidence levels to the sentence-hypothesis produced. These confidence levels can be used by the dialogue manager in order to improve the dialogue, by asking a confirmation to the speaker when a sentence is labeled ambiguous. This research is supported by France Telecom's R&D under the contract 971B427.

Abstract: Availability of large corpora for training language models to develop dialogue systems is rare. Fortunately, for specific dialogue application, many sentences follow a limited number of typical patterns. In a language like French, frequent errors are due to homophones.Three paradigms are proposed in this paper to rescore a trellis of hypothesized words. They are based on sentence patterns detected in the most likely sentence hypothesized in a first recognition phase.

This paper presents a novel technique that allows testing spoken dialogue systems by means of an automatic generation of conversations. The technique permits to easily test spoken dialogue systems under a variety of lab-simulated conditions, as it is easy to vary or change the utterance corpus used to check the performance of the system. The technique is based on the use of a module called user simulator whose purpose is to behave as real users when they interact with dialogue systems. The behaviour of the simulator is decided by means of diverse scenarios that represent the goals of the users. The simulator aim is to achieve the goals set in the scenarios during the interaction with the dialogue system. We have applied the technique to test a dialogue system developed in our lab. The test has been carried out considering different levels of white and babble noise as well as a VTS noise compensation technique. The results prove that the dialogue system performance is worse under the babble noise conditions. The VTS technique has been effective when dealing with noisy utterances and has lead to better experimental results, particularly for the white noise. The technique has permitted to detect problems in the dialogue strategies employed to handle confirmation turns and recognition errors, suggesting that these strategies must be improved. q 2002 Elsevier Science B.V. All rights reserved.