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Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten. Towards a knowledge graph based speech interface
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Fulltext urn:nbn:de:0011-n-4769786 (262 KByte PDF) MD5 Fingerprint: e7d22b0bf95daaffc0c22bc492fddbd5 Created on: 15.12.2017 |
| Salvi, G.: GLU 2017, International Workshop on Grounding Language Understanding. Online resource : 25 August 2017, Stockholm, Sweden Stockholm, 2017 http://www.isca-speech.org/archive/GLU_2017/ DOI: 10.21437/GLU.2017 pp.8-12 |
| International Workshop on Grounding Language Understanding (GLU) <2017, Stockholm> |
| European Commission EC H2020; 642795; WDAqua |
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| English |
| Conference Paper, Electronic Publication |
| Fraunhofer IAIS () |
| Speech Input; Knowledge Graphs; speech recognition; Speech Interface |
Abstract
Applications which use human speech as an input require a speech interface with high recognition accuracy. The words or phrases in the recognized text are annotated with a machine-understandable meaning and linked to knowledge graphs for further processing by the target application. This type of knowledge representation facilitates to use speech interfaces with any spoken input application, since the information is represented in logical, semantic form., retrieving and storing can be followed using any web standard query languages. In this work, we develop a methodology for linking speech input to knowledge graphs. We show that for a corpus with lower WER, the annotation and linking of entities to the DBpedia knowledge graph is considerable. DBpedia Spotlight, a tool to interlink text documents with the linked open data is used to link the speech recognition output to the DBpedia knowledge graph. Such a knowledge-based speech recognition interface is useful for applications such as question answering or spoken dialog systems.