Improving Response Time Through Multimodal Integration Pattern Modeling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00304615" target="_blank" >RIV/68407700:21230/16:00304615 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ISM.2016.9" target="_blank" >http://dx.doi.org/10.1109/ISM.2016.9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ISM.2016.9" target="_blank" >10.1109/ISM.2016.9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving Response Time Through Multimodal Integration Pattern Modeling

Popis výsledku v původním jazyce

While researchers have focused primarily on accuracy when addressing multimodal input segmentation, response time (or latency) has been rather overlooked in their work, despite its unquestionable importance. We propose a method of the input segmentation through integration pattern modeling that provides a significant improvement in response time over the state-of-the-art approaches, while maintaining remarkably high accuracy (98–99%). To this end, a new Bayesian Belief Network classification model was designed based on the recent empirical evidence about users’ multimodal integration patterns. The model is employed in a procedure to segment related inputs into multimodal units. Using the introduced procedure the response time can be improved to 0.8 seconds for sequential integrators and even dropped bellow 0.5 s for simultaneous, which represents a relative improvement of 20% and 50%, resp., at the very least. Although demonstrated on a combination of speech and gestures, the suggested approach can be generalized to a broad range of other modality mixtures.

Název v anglickém jazyce

Improving Response Time Through Multimodal Integration Pattern Modeling

Popis výsledku anglicky

While researchers have focused primarily on accuracy when addressing multimodal input segmentation, response time (or latency) has been rather overlooked in their work, despite its unquestionable importance. We propose a method of the input segmentation through integration pattern modeling that provides a significant improvement in response time over the state-of-the-art approaches, while maintaining remarkably high accuracy (98–99%). To this end, a new Bayesian Belief Network classification model was designed based on the recent empirical evidence about users’ multimodal integration patterns. The model is employed in a procedure to segment related inputs into multimodal units. Using the introduced procedure the response time can be improved to 0.8 seconds for sequential integrators and even dropped bellow 0.5 s for simultaneous, which represents a relative improvement of 20% and 50%, resp., at the very least. Although demonstrated on a combination of speech and gestures, the suggested approach can be generalized to a broad range of other modality mixtures.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2016 IEEE International Symposium on Multimedia

ISBN

978-1-5090-4570-9

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

419-424

Název nakladatele

IEEE Computer Soc.

Místo vydání

Los Alamitos, CA

Místo konání akce

San Jose, California

Datum konání akce

11. 12. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—