A Coarse-Grained Model for Optimal Coupling of ASR and SMT Systems for Speech Translation
نویسندگان
چکیده
Speech translation is conventionally carried out by cascading an automatic speech recognition (ASR) and a statistical machine translation (SMT) system. The hypotheses chosen for translation are based on the ASR system’s acoustic and language model scores, and typically optimized for word error rate, ignoring the intended downstream use: automatic translation. In this paper, we present a coarseto-fine model that uses features from the ASR and SMT systems to optimize this coupling. We demonstrate that several standard features utilized by ASR and SMT systems can be used in such a model at the speech-translation interface, and we provide empirical results on the Fisher Spanish-English speech translation corpus.
منابع مشابه
Investigation on the effects of ASR tuning on speech translation performance
In this paper we describe some of our recent investigations into ASR and SMT coupling issues from an ASR perspective. Our study was motivated by several areas: Firstly, to understand how standard ASR tuning procedures effect the SMT performance and whether it is safe to perform this tuning in isolation. Secondly, to investigate how vocabulary and segmentation mismatches between the ASR and SMT ...
متن کاملStatistical 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 uncertai...
متن کاملUsing Word Lattice Information for a Tighter Coupling in Speech Translation Systems
In this paper we present first experiments towards a tighter coupling between Automatic Speech Recognition (ASR) and Statistical Machine Translation (SMT) to improve the overall performance of our speech translation system. In coventional speech translation systems, the recognizer outputs a single hypothesis which is then translated by the SMT system. This approach has the limitation of being l...
متن کاملUsing word latice information for a tighter coupling in speech translation systems
In this paper we present first experiments towards a tighter coupling between Automatic Speech Recognition (ASR) and Statistical Machine Translation (SMT) to improve the overall performance of our speech translation system. In coventional speech translation systems, the recognizer outputs a single hypothesis which is then translated by the SMT system. This approach has the limitation of being l...
متن کاملAssessing the Impact of Speech Recognition Errors on Machine Translation Quality
In spoken language translation, it is crucial that an automatic speech recognition (ASR) system produces outputs that can be adequately translated by a statistical machine translation (SMT) system. While word error rate (WER) is the standard metric of ASR quality, the assumption that each ASR error type is weighted equally is violated in a SMT system that relies on structured input. In this pap...
متن کامل