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A Spoken Document Retrieval Application in the Oral History Domain

The application of automatic speech recognition in the 
broadcast news domain is well studied. Recognition performance 
is generally high and accordingly, spoken document retrieval 
can successfully be applied in this domain, as demonstrated 
by a number of commercial systems. In other domains, 
a similar recognition performance is hard to obtain, or even far 
out of reach, for example due to lack of suitable training material. 
This is a serious impediment for the successful application 
of spoken document retrieval techniques for other data then 
news. This paper outlines our first steps towards a retrieval system 
that can automatically be adapted to new domains. We discuss 
our experience with a recently implemented spoken document 
retrieval application attached to a web-portal that aims at 
the disclosure of a multimedia data collection in the oral history 
domain. The paper illustrates that simply deploying an off-theshelf 
broadcast news system in this task domain will produce 
error rates that are too high to be useful for retrieval tasks. By 
applying adaptation techniques on the acoustic level and language 
model level, system performance can be improved considerably, 
but additional research on unsupervised adaptation 
and search interfaces is required to create an adequate search 
environment based on speech transcripts.