Transcriber Driving Strategies for Transcription Aid System

Speech recognition technology suffers from a lack of robustness which limits its usability for fully automated speech-to-text transcription, and manual correction is generally required to obtain perfect transcripts. In this paper, we propose a general scheme for semi-automatic transcription, in which the system and the transcriptionist contribute jointly to the speech transcription. The proposed system relies on the editing of confusion networks and on reactive decoding, the latter one being supposed to take benefits from the manual correction and improve the error rates. In order to reduce the correction time, we evaluate various strategies aiming to guide the transcriptionist towards the critical areas of transcripts. These strategies are based on graph density-based criterion and two semantic consistency criterion; using a corpus-based method and a web-search engine. They allow to indicate to the user the areas which present severe lacks of understandability. We evaluate these driving strategies by simulating the correction process of French broadcast news transcriptions. Results show that interactive decoding improves the correction act efficiency with all driving strategies and semantic information must be integrated into the interactive decoding process.