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TUTCRIS

Some background on dialogue management and conversational speech for dialogue systems

Tutkimustuotosvertaisarvioitu

Standard

Some background on dialogue management and conversational speech for dialogue systems. / Wilks, Yorick; Catizone, Roberta; Worgan, Simon; Turunen, Markku.

julkaisussa: Computer Speech and Language, Vuosikerta 25, Nro 2, 04.2011, s. 128-139.

Tutkimustuotosvertaisarvioitu

Harvard

Wilks, Y, Catizone, R, Worgan, S & Turunen, M 2011, 'Some background on dialogue management and conversational speech for dialogue systems', Computer Speech and Language, Vuosikerta. 25, Nro 2, Sivut 128-139. https://doi.org/10.1016/j.csl.2010.03.001

APA

Wilks, Y., Catizone, R., Worgan, S., & Turunen, M. (2011). Some background on dialogue management and conversational speech for dialogue systems. Computer Speech and Language, 25(2), 128-139. https://doi.org/10.1016/j.csl.2010.03.001

Vancouver

Wilks Y, Catizone R, Worgan S, Turunen M. Some background on dialogue management and conversational speech for dialogue systems. Computer Speech and Language. 2011 huhti;25(2):128-139. https://doi.org/10.1016/j.csl.2010.03.001

Author

Wilks, Yorick ; Catizone, Roberta ; Worgan, Simon ; Turunen, Markku. / Some background on dialogue management and conversational speech for dialogue systems. Julkaisussa: Computer Speech and Language. 2011 ; Vuosikerta 25, Nro 2. Sivut 128-139.

Bibtex - Lataa

@article{a8fbd31c155d4fd5b62bd81854c1411a,
title = "Some background on dialogue management and conversational speech for dialogue systems",
abstract = "Several dialogue management (DM) architectures and conversational speech for dialogue systems are presented. Basic types of DM systems include dialogue grammars and frames, plan-based and collaborative systems, and conversational games theory. DM architectures include SmartKom, Trindi, WITAS, CONVERSE, COMIC, agent-based dialogue management, and DM and automatic speech recognition (ASR) language modeling. All data collection tasks should be tailored for the conversational scenario under consideration as each scenario can present different properties. It is shown in the multimodal dialogue system that turn taking can usually be achieved by a fusion of gesture, gaze, and intonation. Intonation within the speech signal informs the dialogue manager when new information is introduced into the current conversation. By placing established emotion detection methods within the recursive nature of conversation we can consider discourse as the exploitation of the shared set of interaction affordances.",
keywords = "Dialogue architectures, Dialogue management, Dialogue systems, Emotion detection, Human-computer interaction",
author = "Yorick Wilks and Roberta Catizone and Simon Worgan and Markku Turunen",
year = "2011",
month = "4",
doi = "10.1016/j.csl.2010.03.001",
language = "English",
volume = "25",
pages = "128--139",
journal = "Computer Speech and Language",
issn = "0885-2308",
publisher = "Elsevier",
number = "2",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Some background on dialogue management and conversational speech for dialogue systems

AU - Wilks, Yorick

AU - Catizone, Roberta

AU - Worgan, Simon

AU - Turunen, Markku

PY - 2011/4

Y1 - 2011/4

N2 - Several dialogue management (DM) architectures and conversational speech for dialogue systems are presented. Basic types of DM systems include dialogue grammars and frames, plan-based and collaborative systems, and conversational games theory. DM architectures include SmartKom, Trindi, WITAS, CONVERSE, COMIC, agent-based dialogue management, and DM and automatic speech recognition (ASR) language modeling. All data collection tasks should be tailored for the conversational scenario under consideration as each scenario can present different properties. It is shown in the multimodal dialogue system that turn taking can usually be achieved by a fusion of gesture, gaze, and intonation. Intonation within the speech signal informs the dialogue manager when new information is introduced into the current conversation. By placing established emotion detection methods within the recursive nature of conversation we can consider discourse as the exploitation of the shared set of interaction affordances.

AB - Several dialogue management (DM) architectures and conversational speech for dialogue systems are presented. Basic types of DM systems include dialogue grammars and frames, plan-based and collaborative systems, and conversational games theory. DM architectures include SmartKom, Trindi, WITAS, CONVERSE, COMIC, agent-based dialogue management, and DM and automatic speech recognition (ASR) language modeling. All data collection tasks should be tailored for the conversational scenario under consideration as each scenario can present different properties. It is shown in the multimodal dialogue system that turn taking can usually be achieved by a fusion of gesture, gaze, and intonation. Intonation within the speech signal informs the dialogue manager when new information is introduced into the current conversation. By placing established emotion detection methods within the recursive nature of conversation we can consider discourse as the exploitation of the shared set of interaction affordances.

KW - Dialogue architectures

KW - Dialogue management

KW - Dialogue systems

KW - Emotion detection

KW - Human-computer interaction

UR - http://www.scopus.com/inward/record.url?scp=78049527943&partnerID=8YFLogxK

U2 - 10.1016/j.csl.2010.03.001

DO - 10.1016/j.csl.2010.03.001

M3 - Review Article

VL - 25

SP - 128

EP - 139

JO - Computer Speech and Language

JF - Computer Speech and Language

SN - 0885-2308

IS - 2

ER -