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ACL
2004
13 years 6 months ago
Combining Acoustic and Pragmatic Features to Predict Recognition Performance in Spoken Dialogue Systems
We use machine learners trained on a combination of acoustic confidence and pragmatic plausibility features computed from dialogue context to predict the accuracy of incoming n-be...
Malte Gabsdil, Oliver Lemon
ANLP
2000
109views more  ANLP 2000»
13 years 6 months ago
Predicting Automatic Speech Recognition Performance Using Prosodic Cues
In spoken dialogue systems, it is important for a system to know how likely a speech recognition hypothesis is to be correct, so it can reprompt for fresh input, or, in cases wher...
Diane J. Litman, Julia Hirschberg, Marc Swerts
NAACL
2010
13 years 2 months ago
Learning about Voice Search for Spoken Dialogue Systems
In a Wizard-of-Oz experiment with multiple wizard subjects, each wizard viewed automated speech recognition (ASR) results for utterances whose interpretation is critical to task s...
Rebecca J. Passonneau, Susan L. Epstein, Tiziana L...
ACL
2001
13 years 6 months ago
Predicting User Reactions to System Error
This paper focuses on the analysis and prediction of so-called aware sites, defined as turns where a user of a spoken dialogue system first becomes aware that the system has made ...
Diane J. Litman, Julia Hirschberg, Marc Swerts
ICASSP
2008
IEEE
13 years 11 months ago
Frame-based acoustic feature integration for speech understanding
With the purpose of improving Spoken Language Understanding (SLU) performance, a combination of different acoustic speech recognition (ASR) systems is proposed. State a-posteriori...
Loic Barrault, Christophe Servan, Driss Matrouf, G...