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        <title>Concept Type Prediction and Responsive Adaptation in a Dialogue System</title>
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    <name type="personal">
        <namePart type="given">Svetlana</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.5087/dad.2012.101</identifier>
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        <date>2012-02</date>
        <detail type="volume"><number>3</number></detail>
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%0 Journal Article
%T Concept Type Prediction and Responsive Adaptation in a Dialogue System
%A Stoyanchev, Svetlana
%A Stent, Amanda J.
%J Dialogue & Discourse
%D 2012
%8 February
%V 3
%I University of Bielefeld
%C Bielefeld, Germany
%F stoyanchev-stent-2012-concept
%X 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.
%R 10.5087/dad.2012.101
%U https://aclanthology.org/2012.dnd-3.9/
%U https://doi.org/10.5087/dad.2012.101
%P 1-31

Markdown (Informal)

[Concept Type Prediction and Responsive Adaptation in a Dialogue System](https://aclanthology.org/2012.dnd-3.9/) (Stoyanchev & Stent, DND 2012)

• Concept Type Prediction and Responsive Adaptation in a Dialogue System (Stoyanchev & Stent, DND 2012)

ACL

• Svetlana Stoyanchev and Amanda J. Stent. 2012. Concept Type Prediction and Responsive Adaptation in a Dialogue System. Dialogue & Discourse, 3:1–31.