Automatic transcription error recovery for person name recognition

Abstract

Person Name Recognition from transcriptions of TV shows spoken content is a crucial step towards multimedia document indexing. Recognizing Person Names implies the combination of three main modules: Automatic Speech Recognition, Named-Entity Recognition and Entity Linking to associate the recognized surface form to a normalized Person Name. The three modules are potentially error prone. Hence, beyond each module's intrinsic complexity, the Person Names issue suffers from the highly dynamic evolution of vocabularies and occurrence contexts that are correlated to various dimensions (such as actuality, topic of the show…). This paper focuses on the first module and proposes an approach to recover from transcription errors made on Person Names. An error correction method is applied on the textual ASR output and we show that it is all the more efficient that it is coupled with a specific error region detection system. Experiments on the French REPERE database show that Person Names transcription can be efficiently corrected while preserving the overall transcription quality and thus increasing the performance of the whole Person Name Recognition process.