Improving the Accuracy of Speech Recognition Systems for Professional Translators
Our principal objective was to reduce the error rate of speech recognition systems used by professional translators. Our work concentrated on Spanish-to-English translation. In a baseline study we estimated the speech recognition error rate of an off-the-shelf recognizer to be 9.98% We describe two independent methods of improving speech recognition systems for translators: a word-for-word translation method and a topic-based method. The topic-based approach performed the best, reducing the error rate significantly, to 5.07%.
Copyright: © 2012 Jha S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The automated construction of computer programs from formal behavioral specifications [1] has been a subject of active interest in the software engineering community for the last forty years. Several approaches for constructing programs from variants of temporal logics have been studied, and practical tools for generating programs from high-level specification languages such as UML have been implemented. However, little effort has been directed towards the automated synthesis of correct-by-construction software by leveraging complementary strengths of formal verification and modern machine learning techniques.