Reconstructing Medical Dictations from Automatically Recognized and Non-Literal Transcripts with Phonetic Similarity Matching

In this paper, we describe the automatic reconstruction of literal transcriptions for medical dictations from a non-literal transcription and an automatically recognized speech transcript by phonetic similarity matching and alignment. We present a customized phonetic similarity measure which is trai...

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Bibliographic Details
Published in2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07 Vol. 4; pp. IV-1125 - IV-1128
Main Authors Petrik, S., Kubin, G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2007
Subjects
Automatic speech recognition
dictation
Error analysis
Error correction
Laboratories
Levenshtein distance
Natural languages
Oral communication
phonetic similarity
Robustness
Signal processing
Speech processing
String edit distance
Text recognition
trained similarity measure
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Summary:In this paper, we describe the automatic reconstruction of literal transcriptions for medical dictations from a non-literal transcription and an automatically recognized speech transcript by phonetic similarity matching and alignment. We present a customized phonetic similarity measure which is trained on a set of phonetically similar string pairs, returns interpretable alignment results, and is robust in its application. Furthermore, we introduce flexible automatic phonetic transcription with regular expressions to deal with formatted entities in written texts and alternative pronunciations in recognized texts. In an evaluation, our method reduced the word error rate for the reconstructed transcription by 12% relative.
ISBN:9781424407279
1424407273
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2007.367272