Concept Type Prediction and Responsive Adaptation in a Dialogue System

Svetlana Stoyanchev, Amanda Stent

Abstract


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.

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www.dialogue-and-discourse.org ISSN: 2152-9620   Journal doi: 10.5087/dad