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Abstract

Architecture that formulates speech translation as a unified log-linear model with a plurality of feature functions, some of which are derived from generative models. The architecture employs discriminative training for the generative features based on an optimization technique referred to as growth transformation. A discriminative training objective function is formulated for speech translation as well as a growth transformation-based model training method that includes an iterative training formula. This architecture is used to design and perform the global end-to-end optimization of speech translation, which when compared with conventional methods for speech translation provides not only a learning method with faster convergence but also improves speech translation accuracy.

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