Matches in ScholarlyData for { ?s ?p ?o. }
- 45 hasAuthorList authorList.
- 45 hasTopic Linguistics.
- 45 isPartOf proceedings.
- 45 keyword "Evaluation methodologies".
- 45 keyword "Information Extraction, Information Retrieval".
- 45 keyword "Speech recognition and understanding".
- 45 title "Combining Multiple Models for Speech Information Retrieval".
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- item-1 hasContent muath-alzghool.
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- 451 creator antton-gurrutxaga.
- 451 creator eli-pociello.
- 451 creator eneko-agirre.
- 451 creator german-rigau.
- 451 creator izaskun-aldezabal.
- 451 type InProceedings.
- 451 label "WNTERM: Enriching the MCR with a Terminological Dictionary".
- 451 sameAs 451.
- 451 abstract "In this paper we describe the methodology and the first steps for the creation of WNTERM (from WordNet and Terminology), a specialized lexicon produced from the merger of the EuroWordNet-based Multilingual Central Repository (MCR) and the Basic Encyclopaedic Dictionary of Science and Technology (BDST). As an example, the ecology domain has been used. The final result is a multilingual (Basque and English) light-weight domain ontology, including taxonomic and other semantic relations among its concepts, which is tightly connected to other wordnets.".
- 451 hasAuthorList authorList.
- 451 hasTopic Linguistics.
- 451 isPartOf proceedings.
- 451 keyword "Lexicon, lexical database".
- 451 keyword "Ontologies".
- 451 keyword "Semantics".
- 451 title "WNTERM: Enriching the MCR with a Terminological Dictionary".
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- item-1 hasContent eli-pociello.
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- 454 creator doroteo-t-toledano.
- 454 creator eduardo-lopez.
- 454 creator guillermo-portillo.
- 454 creator jose-alcazar.
- 454 creator luis-a-hernandez.
- 454 creator ruben-fernandez.
- 454 type InProceedings.
- 454 label "Design of a Multimodal Database for Research on Automatic Detection of Severe Apnoea Cases".
- 454 sameAs 454.
- 454 abstract "The aim of this paper is to present the design of a multimodal database suitable for research on new possibilities for automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases can be very useful to give priority to their early treatment optimizing the expensive and time-consuming tests of current diagnosis methods based on full overnight sleep in a hospital. This work is part of an on-going collaborative project between medical and signal processing groups towards the design of a multimodal database as an innovative resource to promote new research efforts on automatic OSA diagnosis through speech and image processing technologies. In this contribution we present the multimodal design criteria derived from the analysis of specific voice properties related to OSA physiological effects as well as from the morphological facial characteristics in apnoea patients. Details on the database structure and data collection methodology are also given as it is intended to be an open resource to promote further research in this field. Finally, preliminary experimental results on automatic OSA voice assessment are presented for the collected speech data in our OSA multimodal database. Standard GMM speaker recognition techniques obtain an overall correct classification rate of 82%. This represents an initial promising result underlining the interest of this research framework and opening further perspectives for improvement using more specific speech and image recognition technologies.".
- 454 hasAuthorList authorList.
- 454 hasTopic Linguistics.
- 454 isPartOf proceedings.
- 454 keyword "Corpus (creation, annotation, etc.)".
- 454 keyword "Speech resource/database".
- 454 keyword "Tools, systems, applications".
- 454 title "Design of a Multimodal Database for Research on Automatic Detection of Severe Apnoea Cases".
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- item-1 hasContent ruben-fernandez.
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