Statistical Machine Translation and Automatic Speech Recognition under Uncertainty

Statistical modeling techniques have been applied successfully to natural language processing tasks such as automatic speech recognition (ASR) and statistical machine translation (SMT). Since most statistical approaches rely heavily on availability of data and the underlying model assumptions, reduction in uncertainty is critical to their optimal performance. In speech translation, the uncertainty is due to the speech input to the SMT system whose elements are represented as distributions over sequences. A novel approach to statistical phrase-based speech translation is proposed. This approach is based on a generative, source-channel model of translation, similar in spirit to the modeling approaches that underly hidden Markov model(HMM)-based ASR systems: in fact, our model of speech-to-text translation contains the acoustic models of a large vocabulary ASR system as one of its components. This model of speech-totext translation is developed as a direct extension of the phrase-based models used in text translation systems. Speech is translated by mapping ASR word lattices to lattices of phrase sequences which are then translated using operations developed for text translation. Efficient phrase extraction from ASR lattices and word and phrase level pruning strategies for speech translation are investigated to reduce uncertainty in translation of speech. In order to achieve good translation performance it is necessary to find optimal parameters under a particular training objective. Two different discriminative training objective functions are investigated: Maximum Mutual Information (MMI) and Expected BLEU. A novel iterative optimization procedure, using growth transformations is proposed as a parameter update procedure for the training criteria. The translation performance using growth transformation based updates is investigated in detail.