Generalization of Discriminative Approaches for Speech Language Understanding in a Multilingual Context

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.