Abstract
Probabilistic approaches are now widespread in the various applications of natural language processing and elicitation of a particular approach usually depends on the task at hand. Targeting multilingual interpretation of speech, this paper presents a comparison between the state-of-the-art methods used for machine translation and speech understanding. This comparison justifies our proposition of a unified framework to perform a joint decoding which translates a sentence and assigns semantic tags to this translation in the same process. The decoding is achieved using a cascade of finite-state transducers allowing to compose translation and understanding hypothesis graphs. This representation is favorable as it can be generalized to allow rich transmission of information between the components of a human-machine vocal interface.
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Jabaian, B., Lefèvre, F., Besacier, L. (2013). Generalization of Discriminative Approaches for Speech Language Understanding in a Multilingual Context. In: Dediu, AH., MartĂn-Vide, C., Mitkov, R., Truthe, B. (eds) Statistical Language and Speech Processing. SLSP 2013. Lecture Notes in Computer Science(), vol 7978. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39593-2_11
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DOI: https://doi.org/10.1007/978-3-642-39593-2_11
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