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description Publicationkeyboard_double_arrow_right Conference object , Other literature type 2017 United Kingdom, SwitzerlandPublisher:Association for Computational Linguistics (ACL) Funded by:EC | SUMMAEC| SUMMALiepins, R; Germann, U; Barzdins, G; Birch, A; Renals, S; Weber, S; Van Der Kreeft, P; Bourlard, H; Prieto, J; Klejch, O; Bell, P; Lazaridis, A; Mendes, A; Riedel, S; Almeida, MSC; Balage, P; Cohen, S; Dwojak, T; Garner, P; Giefer, A; Junczys-Dowmunt, M; Imran, H; Nogueira, D; Ali, A; Miranda, S; Popescu-Belis, A; Werlen, LM; Papasarantopoulos, N; Obamuyide, A; Jones, C; Dalvi, F; Vlachos, A; Wang, Y; Tong, S; Sennrich, R; Pappas, N; Narayan, S; Damonte, M; Durrani, N; Khurana, S; Abdelali, A; Sajjad, H; Vogel, S; Sheppey, D; Hernon, C; Mitchell, J;We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.
UCL Discovery arrow_drop_down Infoscience - EPFL scientific publicationsConference objectData sources: Infoscience - EPFL scientific publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.18653/v1/e17-3029&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!visibility 46visibility views 46 download downloads 49 Powered bymore_vert UCL Discovery arrow_drop_down Infoscience - EPFL scientific publicationsConference objectData sources: Infoscience - EPFL scientific publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.18653/v1/e17-3029&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Contribution for newspaper or weekly magazine , Preprint , Conference object , Article 2016 United KingdomPublisher:ISCA Funded by:EC | SUMMAEC| SUMMAAhmed Ali; Najim Dehak; Patrick Cardinal; Sameer Khurana; Sree Harsha Yella; James Glass; Peter Bell; Steve Renals;In this paper, we investigate different approaches for dialect identification in Arabic broadcast speech. These methods are based on phonetic and lexical features obtained from a speech recognition system, and bottleneck features using the i-vector framework. We studied both generative and discriminative classifiers, and we combined these features using a multi-class Support Vector Machine (SVM). We validated our results on an Arabic/English language identification task, with an accuracy of 100%. We also evaluated these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We further reported results using the proposed methods to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 59.2%. We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier. All the data used on our experiments have been released to the public as a language identification corpus.
Edinburgh Research E... arrow_drop_down Edinburgh Research ExplorerContribution for newspaper or weekly magazine . 2016Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2015Data sources: arXiv.org e-Print Archivehttps://doi.org/10.21437/Inter...Other literature type . Conference object . 2016Data sources: European Union Open Data Portalhttps://doi.org/10.21437/inter...Conference object . 2016 . Peer-reviewedadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.21437/interspeech.2016-1297&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 47 citations 47 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!more_vert Edinburgh Research E... arrow_drop_down Edinburgh Research ExplorerContribution for newspaper or weekly magazine . 2016Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2015Data sources: arXiv.org e-Print Archivehttps://doi.org/10.21437/Inter...Other literature type . Conference object . 2016Data sources: European Union Open Data Portalhttps://doi.org/10.21437/inter...Conference object . 2016 . Peer-reviewedadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.21437/interspeech.2016-1297&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu
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description Publicationkeyboard_double_arrow_right Conference object , Other literature type 2017 United Kingdom, SwitzerlandPublisher:Association for Computational Linguistics (ACL) Funded by:EC | SUMMAEC| SUMMALiepins, R; Germann, U; Barzdins, G; Birch, A; Renals, S; Weber, S; Van Der Kreeft, P; Bourlard, H; Prieto, J; Klejch, O; Bell, P; Lazaridis, A; Mendes, A; Riedel, S; Almeida, MSC; Balage, P; Cohen, S; Dwojak, T; Garner, P; Giefer, A; Junczys-Dowmunt, M; Imran, H; Nogueira, D; Ali, A; Miranda, S; Popescu-Belis, A; Werlen, LM; Papasarantopoulos, N; Obamuyide, A; Jones, C; Dalvi, F; Vlachos, A; Wang, Y; Tong, S; Sennrich, R; Pappas, N; Narayan, S; Damonte, M; Durrani, N; Khurana, S; Abdelali, A; Sajjad, H; Vogel, S; Sheppey, D; Hernon, C; Mitchell, J;We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring. The platform contains a rich suite of low-level and high-level natural language processing technologies: automatic speech recognition of broadcast media, machine translation, automated tagging and classification of named entities, semantic parsing to detect relationships between entities, and automatic construction / augmentation of factual knowledge bases. Implemented on the Docker platform, it can easily be deployed, customised, and scaled to large volumes of incoming media streams.
UCL Discovery arrow_drop_down Infoscience - EPFL scientific publicationsConference objectData sources: Infoscience - EPFL scientific publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.18653/v1/e17-3029&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 2 citations 2 popularity Average influence Average impulse Average Powered by BIP!visibility 46visibility views 46 download downloads 49 Powered bymore_vert UCL Discovery arrow_drop_down Infoscience - EPFL scientific publicationsConference objectData sources: Infoscience - EPFL scientific publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.18653/v1/e17-3029&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Contribution for newspaper or weekly magazine , Preprint , Conference object , Article 2016 United KingdomPublisher:ISCA Funded by:EC | SUMMAEC| SUMMAAhmed Ali; Najim Dehak; Patrick Cardinal; Sameer Khurana; Sree Harsha Yella; James Glass; Peter Bell; Steve Renals;In this paper, we investigate different approaches for dialect identification in Arabic broadcast speech. These methods are based on phonetic and lexical features obtained from a speech recognition system, and bottleneck features using the i-vector framework. We studied both generative and discriminative classifiers, and we combined these features using a multi-class Support Vector Machine (SVM). We validated our results on an Arabic/English language identification task, with an accuracy of 100%. We also evaluated these features in a binary classifier to discriminate between Modern Standard Arabic (MSA) and Dialectal Arabic, with an accuracy of 100%. We further reported results using the proposed methods to discriminate between the five most widely used dialects of Arabic: namely Egyptian, Gulf, Levantine, North African, and MSA, with an accuracy of 59.2%. We discuss dialect identification errors in the context of dialect code-switching between Dialectal Arabic and MSA, and compare the error pattern between manually labeled data, and the output from our classifier. All the data used on our experiments have been released to the public as a language identification corpus.
Edinburgh Research E... arrow_drop_down Edinburgh Research ExplorerContribution for newspaper or weekly magazine . 2016Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2015Data sources: arXiv.org e-Print Archivehttps://doi.org/10.21437/Inter...Other literature type . Conference object . 2016Data sources: European Union Open Data Portalhttps://doi.org/10.21437/inter...Conference object . 2016 . Peer-reviewedadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.21437/interspeech.2016-1297&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 47 citations 47 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!more_vert Edinburgh Research E... arrow_drop_down Edinburgh Research ExplorerContribution for newspaper or weekly magazine . 2016Data sources: Edinburgh Research ExplorerarXiv.org e-Print ArchiveOther literature type . Preprint . 2015Data sources: arXiv.org e-Print Archivehttps://doi.org/10.21437/Inter...Other literature type . Conference object . 2016Data sources: European Union Open Data Portalhttps://doi.org/10.21437/inter...Conference object . 2016 . Peer-reviewedadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.21437/interspeech.2016-1297&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu