Christian Raymond

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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)
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)
Recently, word embedding representations have been investigated for slot filling in Spoken Language Understanding, along with the use of Neural Networks as classifiers. Neural Networks, especially Recurrent Neural Networks, that are specifically adapted to sequence labeling problems, have been applied successfully on the popular ATIS database. In this work,(More)
The paper addresses the issue of confidence measure reliability provided by automatic speech recognition systems for use in various spoken language processing applications. In this context, a conditional random field (CRF)-based combination of contextual features is proposed to improve wordlevel confidence measures. More precisely, the method consists in(More)
The LUNA corpus is a multi-lingual, multidomain spoken dialogue corpus currently under development that will be used to develop a robust natural spoken language understanding toolkit for multilingual dialogue services. The LUNA corpus will be annotated at multiple levels to include annotations of syntactic, semantic, and discourse information; specialized(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)
In this article, we tackle the problem of speaker role detection from broadcast news shows. In the literature, many proposed solutions are based on the combination of various features coming from acoustic, lexical and semantic information with a machine learning algorithm. Many previous studies mention the use of boosting over decision stumps to combine(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)
† LIA/CNRS University of Avignon BP1228 84911 Avignon cedex 09, France Abstract 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(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)