Using context to improve emotion detection in spoken dialog systems

Jackson Liscombe; Giuseppe Riccardi; Dilek Hakkani-Tür

Using context to improve emotion detection in spoken dialog systems

Jackson Liscombe, Giuseppe Riccardi, Dilek Hakkani-Tür

Research output: Contribution to conference › Paper › peer-review

Abstract

Most research that explores the emotional state of users of spoken dialog systems does not fully utilize the contextual nature that the dialog structure provides. This paper reports results of machine learning experiments designed to automatically classify the emotional state of user turns using a corpus of 5,690 dialogs collected with the "How May I Help You^SM" spoken dialog system. We show that augmenting standard lexical and prosodic features with contextual features that exploit the structure of spoken dialog and track user state increases classification accuracy by 2.6%.

Original language	English (US)
Pages	1845-1848
Number of pages	4
State	Published - 2005
Externally published	Yes
Event	9th European Conference on Speech Communication and Technology - Lisbon, Portugal Duration: Sep 4 2005 → Sep 8 2005

Other

Other	9th European Conference on Speech Communication and Technology
Country/Territory	Portugal
City	Lisbon
Period	9/4/05 → 9/8/05

ASJC Scopus subject areas

General Engineering

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title = "Using context to improve emotion detection in spoken dialog systems",

abstract = "Most research that explores the emotional state of users of spoken dialog systems does not fully utilize the contextual nature that the dialog structure provides. This paper reports results of machine learning experiments designed to automatically classify the emotional state of user turns using a corpus of 5,690 dialogs collected with the {"}How May I Help YouSM{"} spoken dialog system. We show that augmenting standard lexical and prosodic features with contextual features that exploit the structure of spoken dialog and track user state increases classification accuracy by 2.6%.",

author = "Jackson Liscombe and Giuseppe Riccardi and Dilek Hakkani-T{\"u}r",

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AU - Liscombe, Jackson

AU - Riccardi, Giuseppe

AU - Hakkani-Tür, Dilek

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Y1 - 2005

N2 - Most research that explores the emotional state of users of spoken dialog systems does not fully utilize the contextual nature that the dialog structure provides. This paper reports results of machine learning experiments designed to automatically classify the emotional state of user turns using a corpus of 5,690 dialogs collected with the "How May I Help YouSM" spoken dialog system. We show that augmenting standard lexical and prosodic features with contextual features that exploit the structure of spoken dialog and track user state increases classification accuracy by 2.6%.

AB - Most research that explores the emotional state of users of spoken dialog systems does not fully utilize the contextual nature that the dialog structure provides. This paper reports results of machine learning experiments designed to automatically classify the emotional state of user turns using a corpus of 5,690 dialogs collected with the "How May I Help YouSM" spoken dialog system. We show that augmenting standard lexical and prosodic features with contextual features that exploit the structure of spoken dialog and track user state increases classification accuracy by 2.6%.

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M3 - Paper

AN - SCOPUS:33745195010

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T2 - 9th European Conference on Speech Communication and Technology

Y2 - 4 September 2005 through 8 September 2005

ER -

Using context to improve emotion detection in spoken dialog systems

Abstract

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