Semantic and phonetic automatic reconstruction of medical dictations

详细信息    查看全文

• 作者：Stefan Petrik ; Christina Drexel ; Leo Fessler ; Jeremy Jancsary ; Alex ; ra Klein ; Gernot Kubin ; Johannes Matiasek ; Franz Pernkopf ; Harald Trost
• 关键词：Automatic transcription ; Semantics ; Phonetics ; Automatic speech recognition ; Dictation
• 刊名：Computer Speech & Language
• 出版年：2011
• 出版时间：April 2011
• 年：2011
• 卷：25
• 期：2
• 页码：363-385
• 全文大小：813 K

文摘

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.