Postscript Version
Proc. SIGIR 2000, pp. 394 (Athens, Greece, July 2000)
© ACM
The Cambridge University Multimedia Document Retrieval
Demo System
A. Tuerk ¹ S.E. Johnson ¹ P. Jourlin ²,
K. Spärck Jones ² & P.C. Woodland ¹
|
¹ Cambridge University Engineering Department, |
² Cambridge University Computer Laboratory, |
|
Trumpington Street, |
Pembroke Street, |
|
Cambridge, CB2 1PZ, UK. |
Cambridge, CB2 3QG, UK. |
|
{
at233,
sej28,
pcw}@eng.cam.ac.uk |
{
pj207,
ksj}@cl.cam.ac.uk
|
http://svr-www.eng.cam.ac.uk/research/projects/Multimedia_Document_Retrieval
The CU-MDR Demo [3] is
a web based application that allows the user to query a database of
automatically generated transcripts of radio broadcasts that are available on-line.
The system downloads the audio track of British and
American news broadcasts from the Internet
once a day and adds them to its archive. The audio, which
comes in RealAudio format, is first converted into standard
uncompressed format from which a transcription is produced using our
large vocabulary broadcast news recognition engine. This yields a
collection of text and audio documents which can be searched by the user.
The recogniser is similar to the system running in 10 times real time
described in [2]. This system gives a word error
rate of 15.9% on the 1998 Hub4 broadcast news evaluation data. On the
Internet audio used here the run-time is increased due to reduced audio quality
but the general level of transcription accuracy remains high at
approximately 20% word error rate on NPR data.
The information retrieval engine used in the CU-MDR demo is the
benchmark system described in [1].
A windowing/recombination system is used for audio data for which story boundary
information is not available.
Semantic posets
are not automatically applied in searching. Instead they are exploited to
suggest new words to the user for addition to the original
query. Interactive relevance feedback is also available. This allows the
user to mark the documents that contain relevant information and
have the system suggest additional query words that distinguish those
from the non-relevant documents.
The user can either query the audio/text database
interactively, submitting a request by typing a query into the search
field or he/she can specify a set of standing queries that represent his/her
long-term interests. The retrieval engine is run on these
queries only on login. If the retriever finds a document that matches
one of these queries and which has not been seen by the user before,
the query is highlighted. This feature effectively
allows the user to filter the incoming broadcasts.
Figure 1: MDR demo interface
Once the retriever has returned the results for a particular search, a
list of extracts from the returned text documents is created with the
query words highlighted. Each
extract is supposed to represent that part of the broadcast document that is most
relevant to the query.
The user can listen to the part of
the sound source that corresponds to the extract. The whole automatic
transcript can be accessed on a separate web page where the user can
listen to selected parts of the transcription by highlighting them.
This work is partly funded by EPSRC grant GR/L49611. We would also
like to thank Ben Timms and Richard Wareham for their contribution to
the demo interface.
References
- 1
-
S. E. Johnson, P. Jourlin, K. Spärck Jones, and P. C. Woodland.
Spoken Document Retrieval for TREC-8 at Cambridge
University.
[ ps |
pdf |
HTML
]
To appear. In
Proc. TREC-8,
NIST Gaithersburg, MD, 2000.
- 2
-
J. J. Odell, P. C. Woodland, and T. Hain.
The CUHTK-Entropic 10xRT Broadcast News Transcription System.
[ ps |
pdf |
HTML]
In
Proc. DARPA Broadcast News Workshop,
pages 271-275, Herndon, VA, 1999.
- 3
-
A. Tuerk, S. E. Johnson, P. Jourlin, K. Spärck Jones, and P. C. Woodland.
The Cambridge University Multimedia Document Retrieval
Demo System.
[ ps |
pdf |
HTML
]
In Proc. RIAO 2000, Content-Based Multimedia Information
Access, volume 3, pages 14-15, Paris, France, 2000.