Dialogue system juggles topics
Technology Research News
more to speech than just words and sentences. There are half sentences,
stops and restarts, changes of subject and corrections.
It took three decades of speech recognition research to produce
systems good enough to use as interfaces between people and computers. One
way to make speech interfaces better is to find ways for computers to recognize
the context of a conversation.
Researchers from Edinburgh University in Scotland and Stanford University
have built a dialogue management system that promises to improve verbal
communication with computers by giving the machine a sense of the type of
phrase a person is likely to say next.
The approach could be used in a wide variety of speech recognition
systems including telephone-based information systems, interactive entertainment
devices, robots, computer interfaces for the visually impaired, in-car dialogue
applications, and speech interfaces for personal computers.
The Conversational Interface Architecture goes beyond the slot-filling
dialogue systems commonly used for airline ticket booking systems by tracking
multiple conversation threads, said Oliver Lemon, a senior research fellow
at Edinburgh University. Slot-filling dialogue systems prompt users to provide
topic-specific information and listen for keywords that determine the system's
response to the user.
The software follows multithreaded conversations -- those that switch
back and forth between several topics -- without having to be programmed,
regulates particular topics, and uses this information to improve speech
recognition rates, according to the researchers. It also recognizes corrective
fragments -- phrases that correct something a user has just said -- and
it allows users to initiate, extend and correct dialogue threads at any
The system accomplishes this by tracking different types of utterances,
including yes or no answers; who, what, where answers; and corrections like
"I meant the office" and "not the tree."
Tracking the context of a conversation simplifies the speech recognition
task by limiting the range of words the system must attempt to recognize.
Existing dialogue management systems constrain speech recognition choices
by limiting them to a certain topic like city names. The researchers' approach
works with any application, however, because it uses type of utterance rather
than specific topic to constrain speech recognition, said Oliver Lemon,
a senior research fellow at Edinburgh University in Scotland. "The system
anticipates what type of dialogue move the user will say next, and uses
this information to tune speech recognition," he said.
Tracking fine-grained dialogue elements lowered overall speech misrecognition
by 11.5 percent and concept misrecognition by 13.4 percent, according to
The researchers are working to improve their system with better
dialogue modeling software and better ways to parse text, said Lemon. Another
step forward is to use machine learning techniques to automatically optimize
dialogue strategies so that conversational interface systems can adjust
themselves based on experience, he said.
The context-sensitive component of the researchers' system could
be applied to practical applications now, said Lemon. Multithreaded dialogue
management could be used practically within two years, he said.
Lemon's research colleague was Alexander Gruenstein from Stanford
University. The work appeared in the September, 2004 issue of the Association
of Computing Machinery (ACM) Transactions on Computer-Human Interaction
(TOCHI). The research was funded by the Wallenberg Foundation and the Edinburgh-Stanford
Timeline: 2 years
Funding: Private, University
TRN Categories: Human-Computer Interaction
Story Type: News
Related Elements: Technical paper, "Multithreaded Context
for Robust Conversational Interfaces: Context-Sensitive Speech Recognition
and Interpretation of Corrective Fragments," Association of Computing Machinery
(ACM) Transactions on Computer-Human Interaction (TOCHI), September, 2004
April 6/13, 2005
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