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We present a universal approach to uncover and correct systematic local errors in complex speech-to-text systems. Whereas previous work to minimize speech recognition errors mostly relies on N-best lists or word lattices, our approach is merely based on the first-best system output. The paradigm of Transformation-Based Learning (TBL) is adapted from(More)
Automatic speech recognition (ASR) has become a valuable tool in large document production environments like medical dictation. While manual post-processing is still needed for correcting speech recognition errors and for creating documents which adhere to various stylistic and formatting conventions, a large part of the document production process is(More)
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