Lucía Ortega

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In this paper we propose an algorithm to learn statistical language understanding models from a corpus of unaligned pairs of sentences and their corresponding semantic representation. Specifically, it allows to automatically map variable-length word segments with their corresponding semantic units and thus, the decoding of user utterances to their(More)
In this paper we present a algorithm for the statistical learning of semantic models, based on a corpus of unaligned pairs of sentences and semantic representations in terms of frames. The objective is automatically associate variable-length segments with their corresponding semantic labels to be used in speech understanding tasks. One advantage of this(More)
In this paper we present a algorithm for the statistical learning of semantic models, based on a corpus of unaligned pairs of sentences and semantic representations in terms of frames. The objective is automatically associate variable-length segments with their corresponding semantic labels to be used in speech understanding tasks. One advantage of this(More)
We present in this paper a prototype of a spoken dialog system. One of the characteristics of this system is that most of the modules (speech recognition, understanding and dialog manager) are based on statistical models. The system has the possibility of easily change the task or the language by means of interchanging the different modules. We present in(More)
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