期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2009
卷号：2009
出版社：ACL Anthology
摘要：We use a machine learner trained on a
combination of acoustic and contextual
features to predict the accuracy of incoming
n-best automatic speech recognition
(ASR) hypotheses to a spoken dialogue
system (SDS). Our novel approach is to
use a simple statistical User Simulation
(US) for this task, which measures the
likelihood that the user would say each
hypothesis in the current context. Such
US models are now common in machine
learning approaches to SDS, are trained on
real dialogue data, and are related to theories
of “alignment” in psycholinguistics.
We use a US to predict the user’s next dialogue
move and thereby re-rank n-best hypotheses
of a speech recognizer for a corpus
of 2564 user utterances. The method
achieved a significant relative reduction of
Word Error Rate (WER) of 5% (this is
44% of the possible WER improvement
on this data), and 62% of the possible semantic
improvement (Dialogue Move Accuracy),
compared to the baseline policy
of selecting the topmost ASR hypothesis.
The majority of the improvement is attributable
to the User Simulation feature,
as shown by Information Gain analysis.
