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Spoken Language Understanding (SLU) for conversational systems (SDS) aims at extracting concept and their relations from spontaneous speech. Previous approaches to SLU have modeled concept relations as stochastic semantic networks ranging from generative approach to discriminative. As spoken dialog systems complexity increases, SLU needs to perform(More)
The extraction of flat concepts out of a given word sequence is usually one of the first steps in building a spoken language understanding (SLU) or dialogue system. This paper explores five different modelling approaches for this task and presents results on a French state-of-the-art corpus, MEDIA. Additionally, two log-linear modelling approaches could be(More)
—One of the first steps in building a spoken language understanding (SLU) module for dialogue systems is the extraction of flat concepts out of a given word sequence, usually provided by an automatic speech recognition (ASR) system. In this paper, six different modeling approaches are investigated to tackle the task of concept tagging. These methods include(More)
A spoken language understanding (SLU) system is described. It generates hypotheses of conceptual constituents with a translation process. This process is performed by finite state transducers (FST) which accept word patterns from a lattice of word hypotheses generated by an Automatic Speech Recognition (ASR) system. FSTs operate in parallel and may share(More)
Within the framework of the French evaluation program MEDIA on spoken dialogue systems, this paper presents the methods proposed at the LIA for the robust extraction of basic conceptual constituents (or concepts) from an audio message. The conceptual decoding model proposed follows a stochastic paradigm and is directly integrated into the Automatic Speech(More)
A search methodology is proposed for performing conceptual decoding process. Such a process provides the best sequence of word hypotheses according to a set of conceptual interpretations. The resulting models are combined in a network of Stochastic Finite State Transducers. This approach is a framework that tries to bridge the gap between speech recognition(More)
Automatic concept segmentation and labeling are the fundamental problems of Spoken Language Understanding in dialog systems. Such tasks are usually approached by using gen-erative or discriminative models based on n-grams. As the uncertainty or ambiguity of the spoken input to dialog system increase, we expect to need dependencies beyond n-gram statistics.(More)
This paper presents a semantic interpretation strategy, for Spoken Dialogue Systems, including an error correction process. Semantic interpretations output by the Spoken Understanding module may be incorrect, but some semantic components may be correct. A set of situations will be introduced, describing semantic confidence based on the agreement of semantic(More)
Across language portability of a spoken language understanding system (SLU) deals with the possibility of reusing with moderate effort in a new language knowledge and data acquired for another language. The approach proposed in this paper is motivated by the availability of the fairly large MEDIA corpus carefully transcribed in French and semantically(More)