Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Danny Merkx"'
Autor:
Mark Hasegawa-Johnson, Lucas Ondel, Elin Larsen, Shruti Palaskar, Liming Wang, Sebastian Stüker, Francesco Ciannella, Markus Müller, Odette Scharenborg, Rachid Riad, Florian Metze, Pierre Godard, Laurent Besacier, Mingxing Du, Alan W. Black, Danny Merkx, Emmanuel Dupoux, Philip Arthur, Graham Neubig
Publikováno v:
IEEE/ACM Transactions on Audio, Speech and Language Processing
IEEE/ACM Transactions on Audio, Speech and Language Processing, 2020, ⟨10.1109/TASLP.2020.2973896⟩
IEEE/ACM Transactions on Audio Speech and Language Processing, 28, 964-975
IEEE/ACM Transactions on Audio Speech and Language Processing, 28, pp. 964-975
IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TASLP.2020.2973896⟩
IEEE-ACM Transactions on Audio, Speech, and Language Processing, 28
IEEE/ACM Transactions on Audio, Speech and Language Processing, 2020, ⟨10.1109/TASLP.2020.2973896⟩
IEEE/ACM Transactions on Audio Speech and Language Processing, 28, 964-975
IEEE/ACM Transactions on Audio Speech and Language Processing, 28, pp. 964-975
IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2020, ⟨10.1109/TASLP.2020.2973896⟩
IEEE-ACM Transactions on Audio, Speech, and Language Processing, 28
International audience; Speech technology plays an important role in our everyday life. Speech is, among others, used for human-computer interaction, including, for instance, information retrieval and on-line shopping. In the case of an unwritten lan
Publikováno v:
Cognitive Computation, 15(1)
Cognitive Computation
Cognitive Computation
Many computational models of speech recognition assume that the set of target words is already given. This implies that these models learn to recognise speech in a biologically unrealistic manner, i.e. with prior lexical knowledge and explicit superv
Publikováno v:
ISCAS
2021 IEEE International Symposium on Circuits and Systems (ISCAS)
2021 IEEE International Symposium on Circuits and Systems (ISCAS)
We investigated word recognition in a Visually Grounded Speech model. The model has been trained on pairs of images and spoken captions to create visually grounded embeddings which can be used for speech to image retrieval and vice versa. We investig
Publikováno v:
Proceedings of Interspeech 2021, pp. 4393-4397
Proceedings of Interspeech 2021, 4393-4397. [S.l.] : ISCA
STARTPAGE=4393;ENDPAGE=4397;TITLE=Proceedings of Interspeech 2021
Proceedings of Interspeech 2021
Proceedings of Interspeech 2021, 4393-4397. [S.l.] : ISCA
STARTPAGE=4393;ENDPAGE=4397;TITLE=Proceedings of Interspeech 2021
Proceedings of Interspeech 2021
This study addresses the question whether visually grounded speech recognition (VGS) models learn to capture sentence semantics without access to any prior linguistic knowledge. We produce synthetic and natural spoken versions of a well known semanti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::819ca8d0e6082bcc41da661980f4aa11
Autor:
Danny Merkx, Stefan L. Frank
Publikováno v:
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL), pp. 12-22
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL), 12-22. online : Association for Computational Linguistics
STARTPAGE=12;ENDPAGE=22;TITLE=Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL)
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2021)
CMLS
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL), 12-22. online : Association for Computational Linguistics
STARTPAGE=12;ENDPAGE=22;TITLE=Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL)
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2021)
CMLS
Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The recently introduced Transformer architecture outperforms RNNs on many natural language processing tasks but little
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4a12282f8d089e3eaa3869d5cf68c56
Publikováno v:
Proceedings of Interspeech 2019
Proceedings of Interspeech 2019. Crossroads of Speech and Language, 1841-1845. [S.l.] : ISCA
STARTPAGE=1841;ENDPAGE=1845;TITLE=Proceedings of Interspeech 2019. Crossroads of Speech and Language
Proceedings of Interspeech 2019. Crossroads of Speech and Language, pp. 1841-1845
INTERSPEECH
Proceedings of Interspeech 2019. Crossroads of Speech and Language, 1841-1845. [S.l.] : ISCA
STARTPAGE=1841;ENDPAGE=1845;TITLE=Proceedings of Interspeech 2019. Crossroads of Speech and Language
Proceedings of Interspeech 2019. Crossroads of Speech and Language, pp. 1841-1845
INTERSPEECH
Humans learn language by interaction with their environment and listening to other humans. It should also be possible for computational models to learn language directly from speech but so far most approaches require text. We improve on existing neur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f04ad864b53d6b288116a0e6e01fe0bf
https://doi.org/10.21437/interspeech.2019-3067
https://doi.org/10.21437/interspeech.2019-3067
Learning semantic sentence representations from visually grounded language without lexical knowledge
Autor:
Danny Merkx, Stefan L. Frank
Publikováno v:
Natural Language Engineering, 25, 4, pp. 451-466
Natural Language Engineering
Natural Language Engineering, 25, 451-466
Natural Language Engineering
Natural Language Engineering, 25, 451-466
Current approaches to learning semantic representations of sentences often use prior word-level knowledge. The current study aims to leverage visual information in order to capture sentence level semantics without the need for word embeddings. We use
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c63f5de52947e262048ef898c3ef6de4
https://hdl.handle.net/2066/205977
https://hdl.handle.net/2066/205977
Autor:
Danny Merkx, Odette Scharenborg
Publikováno v:
Proceedings of Interspeech 2018
INTERSPEECH
INTERSPEECH
The ultimate goal of our research is to improve an existing speech-based computational model of human speech recognition on the task of simulating the role of fine-grained phonetic information in human speech processing. As part of this work we are i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b3e8ca8394228d23d2f5324b78ec57b
https://hdl.handle.net/21.11116/0000-000B-639A-821.11116/0000-000B-639C-6
https://hdl.handle.net/21.11116/0000-000B-639A-821.11116/0000-000B-639C-6
Autor:
Liming Wang, Graham Neubig, Danny Merkx, Florian Metze, Odette Scharenborg, Rachid Riad, Philip Arthur, Lucas Ondel, Sebastian Stüker, Shruti Palaskar, Markus Müller, Laurent Besacier, Mark Hasegawa-Johnson, Emmanuel Dupoux, Elin Larsen, Alan W. Black, Francesco Ciannella, Mingxing Du, Pierre Godard
Publikováno v:
HAL
ICASSP 2018-IEEE International Conference on Acoustics, Speech and Signal Processing
ICASSP 2018-IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Alberta, Canada
ICASSP
ICASSP 2018-IEEE International Conference on Acoustics, Speech and Signal Processing
ICASSP 2018-IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Alberta, Canada
ICASSP
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography. We study the replacement of orth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d46ae9985fa5c1c05ed9dbbbc9526906
Autor:
Odette Scharenborg, Danny Merkx
Publikováno v:
Proceedings of the Machine Learning in Speech and Language Processing Workshop (MLSLP 2018)
Delft University of Technology
Proceedings of the Machine Learning in Speech and Language Processing Workshop
Delft University of Technology
Proceedings of the Machine Learning in Speech and Language Processing Workshop
Fine-Tracker is a speech-based model of human speech recognition. While previous work has shown that Fine-Tracker is successful at modelling aspects of human spoken-word recognition, its speech recognition performance is not comparable to that of hum
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::0027803194d39da5d5ffb429f16ff8ef
https://hdl.handle.net/21.11116/0000-000B-63C6-621.11116/0000-000B-63C8-4
https://hdl.handle.net/21.11116/0000-000B-63C6-621.11116/0000-000B-63C8-4