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 . Named Entity Recognition ( NER ) is a crucial step in many Information Extraction ( IE ) tasks . It has been a specific task in several evaluation pro-grams such as the Message Understanding Conferences ( MUC ) , the Conferences on Natural Language Learning ( CoNLL ) , the DARPA HUB-5 program or more recently the French ESTER Rich Transcription program on Broadcast News data . Most of these conferences have studied the impact of using transcripts generated by an Automatic Speech Recognition ( ASR ) system rather than written texts . It appears from these studies that unlike other IE tasks , NER performance is greatly affected by the Word Error Rate ( WER ) of the transcripts processed . To tackle this problem , different ideas have been proposed : modeling explicitly the ASR errors ( Palmer and Ostendorf , 2001 ) or using the ASR system alternate hypotheses found in word lattices ( Saraclar and Sproat , 2004 ) . However performance in NER decreases dramatically when processing high WER transcripts like the ones that are obtained with unmatched conditions between the ASR training model and the data to process . This paper investigates this phenomenon in the framework of the NER task of the French Rich Transcription program of Broadcast News ESTER ( Gravier et al . , 2004 ) . Several issues are addressed : • how to jointly optimize the ASR and the NER models ? • what is the impact in term of ASR and NER performance of a temporal mismatch between the corpora used to train and test the models and how can it be recovered by means of metadata information ? • Can metadata information be used for indexing large spoken archives ? After a quick overview of related works in IE from speech input , we present the ESTER evaluation program ; then we introduce a NER system tightly integrated to the ASR process and show how it can successfully index high WER spoken databases thanks to metadata . We have presented in this paper a robust Named Entity Recognition system dedicated to process ASR transcripts . The FSM-based approach allows us to control the generalization capabilities of the system while the statistical tagger provides good labeling decisions . The main feature of this system is its ability to extract n-best lists of NE hypothesis from word lattices leaving the decision strategy choosing to either emphasize the recall or the precision of the extraction , according to the task targeted . A comparison between this approach and a standard approach based on the NLP tools Lingpipe validates our hypotheses . This integration of the ASR and the NER processes is particularly important in difficult conditions like those that can be found in large spoken archives where the training corpus does not match all the documents to process . A study of the use of metadata information in order to adapt the ASR and NER models to a specific situation showed that if the overall improvement is small , some salient information related to the metadata added can be better extracted by means of this adaptation .

Challenge: Existing named entity recognition models for speech input process the output from automatic speech recognition disjointly making the process sensitive to the word error rate.
Approach: They propose a system which is optimized to process transcripts and can be integrated into the search process of the automatic speech recognition modules.
Outcome: The proposed method allows the control over generalization capabilities and comparison with a standard approach shows its effectiveness.