Postscript Version
Proc. RIAO 2000, Content-Based Multimedia Information Access, Vol. 3, (Applications) pp. 14-15 (Paris, France, April 2000)
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
|
The Cambridge University Multimedia Document Retrieval Demo System 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 paper describes how
speech recognition and information retrieval techniques are combined
in this system and shows how the user can interact with it.
To provide content-specific access to the vast amount of text data
that are available on the Internet, search engines have been developed
that operate on text documents of various formats (e.g. html). Since there is an increasing
amount of audio data containing speech on the Internet, a similar
device is desirable that operates automatically on audio streams
without the need of manual transcription. The Cambridge University
Multimedia Document Retrieval (CU-MDR) demo system tries to fill this
gap.
The CU-MDR demo system downloads the audio track of news broadcasts from the Internet
once a day and adds them to its archive. The audio, which
usually 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. A request by a user triggers a search on the
collection of text documents. The returned documents can then be
browsed as both text and audio.
The recogniser is similar to the system running in 10 times real time
described in [Odell et al., 1999]. The audio stream is first split into
homogeneous segments. A two pass recogniser is then used to generate
the transcriptions for each segment. The first pass produces a rough
transcription and then unsupervised model adaptation is used to
generate more accurate models for each cluster of acoustic
segments. These adapted models are then used with a 4-gram language
model to generate the final output. The system is trained on about 150
hours of acoustic training data and 260 million words of broadcast
news and newspaper transcriptions. The 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.
The information retrieval engine used in the CU-MDR demo is the
benchmark system described in [Johnson et al., 2000]. Semantic posets [Jourlin et al., 1999]
are not automatically included in this system. Instead they are used to
suggest additional words that can be added to the
original query. 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. When activated both query expansion methods
bring up a list from which the user can select the words that
he/she believes are most useful in expanding the query.
The user gains access to the MDR demo by visiting the MDR demo web
page using a conventional (preferably 4th generation) browser. After
registering with the system the user can query the audio/text database.
This can be done in two ways. Either the user queries the system
interactively, submitting a request by typing a query into the search
field or he/she can specify a set of queries that represent his/her
long-term interests. The retrieval engine is run on these long-term
queries only on login. If the retriever finds a document that matches
one of these queries and which has been added to the data base since
the user's last session, the query is highlighted. This feature effectively
allows the user to filter the incoming broadcasts.
Once the retriever has returned the results for a particular search, a
list of extracts from the returned text documents is created. Each
extract is designed to represent the part of the document that is most
relevant to the query. This list can be sorted using different
criteria, e.g. highest relevance score first, most recent first. Each
extract highlights the query words and also shows how often the query
words were found in the whole document. The ratio of the relevance
score for a document to the score of the top ranking document for the
current search is also displayed. 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.
At the moment the CU-MDR database consists of pre-segmented NPR broadcasts
only. Work is ongoing to extend the data base to British English
news. Also a windowing system is being developed that allows automatic
content dependent segmentation of news broadcasts.
This work is in part funded by an EPSRC grant reference GR/L49611.
- Johnson et al., 2000
-
Johnson, S. E., Jourlin, P., Spärck Jones, K., and Woodland, P. C. (2000).
Spoken Document Retrieval for TREC-8 at Cambridge
University.
[ ps
| HTML |
pdf
]
To appear. In
Proc. TREC-8,
NIST Gaithersburg, MD.
- Jourlin et al., 1999
-
Jourlin, P., Johnson, S. E., Spärck Jones, K., and Woodland, P. C. (1999).
General Query Expansion Techniques for Spoken Document Retrieval.
[ ps
| HTML |
pdf]
In
Proc.
ESCA Workshop on Extracting Information from Spoken
Audio, pages 8-13, Cambridge, England.
- Odell et al., 1999
-
Odell, J. J., Woodland, P. C., and Hain, T. (1999).
The CUHTK-Entropic 10xRT Broadcast News Transcription System.
[ ps |
pdf |
HTML]
In
Proc. DARPA Broadcast News Workshop, pages 271-275,
Herndon, VA.