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Towards a knowledge graph based speech interface

: Jaya Kumar, Ashwini; Auer, Sören; Schmidt, Christoph Andreas; Köhler, Joachim

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
DOI: 10.21437/GLU.2017
International Workshop on Grounding Language Understanding (GLU) <2017, Stockholm>
European Commission EC
H2020; 642795; WDAqua
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
Speech Input; Knowledge Graphs; speech recognition; Speech Interface

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.