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The Speaker and Content Adaptation in Radiology Information System

After attempts on applying the speech recognition system to radiology information system (RIS), we focus on the implementation of introducing adaptation technology to the system. To be consistent with the practical application of RIS, we intend to solve the problems on how to adapt and add new content related to the RIS report to the old model. This paper describes an acoustic model-based speaker and content adaptation scheme using a synthetic method which introduces a simplified maximum likelihood linear regression (MLLR) module to the incremental maximum a posteriori (MAP) processing. We designed and tested a procedure using our own adaptation data collected from hospital diagnostic reports. Finally, the efficiency of this method is supported by experimental results.