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Applying Neural Network Techniques for Topic Change Detection in the HuComTech Corpus
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. MTA SZTE Research Group on Artificial Intelligence, Szeged, Hungary. (Machine Learning)ORCID iD: 0000-0002-0546-116X
Institute of Philosophy, University of Debrecen, Debrecen, Hungary.
2019 (English)In: The Temporal Structure of Multimodal Communication / [ed] László Hunyadi, István Szekrényes, Switzerland: Springer Nature , 2019, p. 147-162Chapter in book (Refereed)
Abstract [en]

In the age of The Internet we are generating documents (both written and spoken) at an unprecedented rate. This rate of document creation—as well as the number of already existing documents—makes manual processing time-consuming and costly to the point of infeasibility. This is the reason why we are in need of automatic methods that are suitable for the processing of written as well as spoken documents. One crucial part of processing documents is partitioning said documents into different segments based on the topic being discussed. A self-evident application of this would be for example partitioning a news broadcast into different news stories. One of the first steps of doing so would be identifying the shifts in the topic framework, or in other words, finding the time-interval where the announcer is changing from one news story to the next. Naturally, as the transition between news stories are often accompanied by easily identifiable audio—(e.g. signal) and visual (e.g. change in graphics) cues, this would not be a particularly different task. However, in other cases the solution to this problem would be far less obvious. Here, we approach this task for the case of spoken dialogues (interviews). One particular difficulty of these dialogues is that the interlocutors often switch between languages. Because of this (and in the hope of contributing to the generality of our method) we carried out topic change detection in a content-free manner, focusing on speaker roles, and prosodic features. For the processing of said features we will employ neural networks, and will demonstrate that using the proper classifier combination methods this can lead to a detection performance that is competitive with that of the state-of-the-art.

Place, publisher, year, edition, pages
Switzerland: Springer Nature , 2019. p. 147-162
Series
Intelligent Systems Reference Library, ISSN 1868-4394, E-ISSN 1868-4408 ; 164
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-75429DOI: 10.1007/978-3-030-22895-8_8Scopus ID: 2-s2.0-85070108014ISBN: 978-3-030-22894-1 (print)ISBN: 978-3-030-22895-8 (print)OAI: oai:DiVA.org:ltu-75429DiVA, id: diva2:1341237
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2020-02-03Bibliographically approved

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Publisher's full textScopushttps://link.springer.com/chapter/10.1007/978-3-030-22895-8_8

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Kovács, G

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