Raquel Justo


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A comparison of linguistically and statistically enhanced models for speech-to-speech machine translation
Alicia Pérez | Víctor Guijarrubia | Raquel Justo | M. Inés Torres | Francisco Casacuberta
Proceedings of the Fourth International Workshop on Spoken Language Translation

The goal of this work is to improve current translation models by taking into account additional knowledge sources such as semantically motivated segmentation or statistical categorization. Specifically, two different approaches are discussed. On the one hand, phrase-based approach, and on the other hand, categorization. For both approaches, both statistical and linguistic alternatives are explored. As for translation framework, finite-state transducers are considered. These are versatile models that can be easily integrated on-the-fly with acoustic models for speech translation purposes. In what the experimental framework concerns, all the models presented were evaluated and compared taking confidence intervals into account.


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Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA
José-Miguel Benedí | Eduardo Lleida | Amparo Varona | María-José Castro | Isabel Galiano | Raquel Justo | Iñigo López de Letona | Antonio Miguel
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

In the framework of the DIHANA project, we present the acquisitionprocess of a spontaneous speech dialogue corpus in Spanish. Theselected application consists of information retrieval by telephone for nationwide trains. A total of 900 dialogues from 225 users were acquired using the “Wizard of Oz” technique. In this work, we present the design and planning of the dialogue scenes and the wizard strategy used for the acquisition of the corpus. Then, we also present the acquisition tools and a description of the acquisition process.