Robust Named Entity Extraction from Large Spoken Archives

Traditional approaches to Information Extraction (IE) from speech input simply consist in applying text based methods to the output of an Automatic Speech Recognition (ASR) system. If it gives satisfaction with low Word Error Rate (WER) transcripts, we believe that a tighter integration of the IE and ASR modules can increase the IE performance in more difficult conditions. More specifically this paper focuses on the robust extraction of Named Entities from speech input where a temporal mismatch between training and test corpora occurs. We describe a Named Entity Recognition (NER) system, developed within the French Rich Broadcast News Transcription program ESTER, which is specifically optimized to process ASR transcripts and can be integrated into the search process of the ASR modules. Finally we show how some metadata information can be collected in order to adapt NER and ASR models to new conditions and how they can be used in a task of Named Entity indexation of spoken archives.