Phonetic string matching: lessons from information retrieval
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Measures of semantic similarity and relatedness in the biomedical domain
Journal of Biomedical Informatics
A methodology of error detection: improving speech recognition in radiology
A methodology of error detection: improving speech recognition in radiology
A new method for OOV detection using hybrid word/fragment system
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
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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 carried out by the ASR system. For improving the quality of the system output, knowledge about the multi-layered relationship between the dictated texts and the final documents is required. Thus, typical speech-recognition errors can be avoided, and proper style and formatting can be anticipated in the ASR part of the document production process. Yet - while vast amounts of recognition results and manually edited final reports are constantly being produced - the error-free literal transcripts of the actually dictated texts are a scarce and costly resource because they have to be created by manually transcribing the audio files. To obtain large corpora of literal transcripts for medical dictation, we propose a method for automatically reconstructing them from draft speech-recognition transcripts plus the corresponding final medical reports. The main innovative aspect of our method is the combination of two independent knowledge sources: phonetic information for the identification of speech-recognition errors and semantic information for detecting post-editing concerning format and style. Speech recognition results and final reports are first aligned, then properly matched based on semantic and phonetic similarity, and finally categorised and selectively combined into a reconstruction hypothesis. This method can be used for various applications in language technology, e.g., adaptation for ASR, document production, or generally for the development of parallel text corpora of non-literal text resources. In an experimental evaluation, which also includes an assessment of the quality of the reconstructed transcripts compared to manual transcriptions, the described method results in a relative word error rate reduction of 7.74% after retraining the standard language model with reconstructed transcripts.