Open AccessConcept Type Prediction and Responsive Adaptation in a Dialogue System
Author(s) -
Svetlana Stoyanchev,
Amanda Stent
Publication year - 2012
Publication title -
dialogue and discourse
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.25
H-Index - 5
ISSN - 2152-9620
DOI - 10.5087/dad.2012.101
Subject(s) - utterance , dialog box , computer science , adaptation (eye) , natural language processing , dialog system , speech recognition , context (archaeology) , artificial intelligence , task (project management) , psychology , paleontology , management , neuroscience , world wide web , economics , biology
Responsive adaptation in spoken dialog systems involves a change in dialog system behavior in response to a user or a dialog situation. In this paper we address responsive adaptation in the automatic speech recognition (ASR) module of a spoken dialog system. We hypothesize that information about the content of a user utterance may help improve speech recognition for the utterance. We use a two-step process to test this hypothesis: first, we automatically predict the task-relevant concept types likely to be present in a user utterance using features from the dialog context and from the output of first-pass ASR of the utterance; and then, we adapt the ASR's language model to the predicted content of the user's utterance and run a second pass of ASR. We show that: (1) it is possible to achieve high accuracy in determining presence or absence of particular concept types in a post-confirmation utterance; and (2) 2-pass speech recognition with concept type classification and language model adaptation can lead to improved speech recognition performance for post-confirmation utterances.