المؤلفون: Jonathan Wenstrup, Jakob Drachmann Havtorn, Lasse Borgholt, Stig Nikolaj Blomberg, Lars Maaloe, Michael R. Sayre, Hanne Christensen, Christina Kruuse, Helle Collatz Christensen

المصدر: npj Digital Medicine, Vol 6, Iss 1, Pp 1-8 (2023)

مصطلحات موضوعية: Computer applications to medicine. Medical informatics, R858-859.7

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2398-6352

URL الوصول: https://doaj.org/article/32a67596e26b4c91b7ed8fcb17e4d93d

المؤلفون: Abdelrahman Mohamed, Hung-yi Lee, Lasse Borgholt, Jakob D. Havtorn, Joakim Edin, Christian Igel, Katrin Kirchhoff, Shang-Wen Li, Karen Livescu, Lars Maaloe, Tara N. Sainath, Shinji Watanabe

المصدر: Mohamed, A, Lee, H, Borgholt, L, Havtorn, J D, Edin, J, Igel, C, Kirchhoff, K, Li, S-W, Livescu, K, Maaløe, L, Sainath, T N & Watanabe, S 2022, ' Self-Supervised Speech Representation Learning: A Review ', IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 6, pp. 1179-1210 . https://doi.org/10.1109/JSTSP.2022.3207050

مصطلحات موضوعية: Self-supervised learning, FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Computation and Language, Data models, Representation learning, Computer Science - Sound, Speech processing, Audio and Speech Processing (eess.AS), Signal Processing, FOS: Electrical engineering, electronic engineering, information engineering, Training, Hidden Markov models, Electrical and Electronic Engineering, Computation and Language (cs.CL), Electrical Engineering and Systems Science - Audio and Speech Processing

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::545143f4d8a2d73ad2a084dd4dafe28e
http://arxiv.org/abs/2205.10643

المؤلفون: Havtorn, Jakob D., Lasse Borgholt, Søren Hauberg, Jes Frellsen, Lars Maaløe

المصدر: Havtorn, J D, Borgholt, L, Hauberg, S, Frellsen, J & Maaløe, L 2022, Benchmarking Generative Latent Variable Models for Speech . in Proceedings of ICLR Workshop on Deep Generative Models for Highly Structured Data . ICLR Workshop on Deep Generative Models for Highly Structured Data, 29/04/2022 .
Technical University of Denmark Orbit

مصطلحات موضوعية: FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Audio and Speech Processing (eess.AS), Computer Science - Artificial Intelligence, Statistics - Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Machine Learning (stat.ML), Computer Science - Sound, Machine Learning (cs.LG), Electrical Engineering and Systems Science - Audio and Speech Processing

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99ca19ca48f1b74959b7255395178b34
http://arxiv.org/abs/2202.12707

المؤلفون: Željko Agić, Anders Søgaard, Christian Igel, Lasse Borgholt, Jakob D. Havtorn, Lars Maaløe

المصدر: Borgholt, L, Havtorn, J D, Agic, Ž, Søgaard, A, Maaløe, L & Igel, C 2020, Do end-to-end speech recognition models care about context? in Proceedings of the Annual Conference of the International Speech Communication Association . Proceedings of the Annual Conference of the International Speech Communication Association, Interspeech, pp. 4352-4356, Interspeech 2020, Shanghai, China, 25/10/2020 . https://doi.org/10.21437/Interspeech.2020-1750
Borgholt, L, Havtorn, J D, Agic, Ž, Søgaard, A, Maaløe, L & Igel, C 2020, Do end-to-end speech recognition models care about context? in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH . vol. 2020-October, International Speech Communication Association (ISCA), pp. 4352-4356, 21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020, Shanghai, China, 25/10/2020 . https://doi.org/10.21437/Interspeech.2020-1750
INTERSPEECH

مصطلحات موضوعية: FOS: Computer and information sciences, Sound (cs.SD), Computer Science - Machine Learning, Computer science, Speech recognition, Context (language use), 010501 environmental sciences, 01 natural sciences, Computer Science - Sound, Machine Learning (cs.LG), Attention-based encoder-decoder, 03 medical and health sciences, 0302 clinical medicine, End-to-end principle, Connectionism, Connectionist temporal classification, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Sensitivity (control systems), 0105 earth and related environmental sciences, Computer Science - Computation and Language, Automatic speech recognition, Contrast (statistics), 030208 emergency & critical care medicine, Language model, End-to-end speech recognition, Computation and Language (cs.CL), Electrical Engineering and Systems Science - Audio and Speech Processing

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f03bc76f832fdcf78bda18a84982ee8
http://arxiv.org/abs/2102.09928

المؤلفون: Jakob D. Havtorn, Tycho Max Sylvester Tax, Christian Igel, Lars Maaløe, Lasse Borgholt

المصدر: Borgholt, L, Tax, T M S, Havtorn, J D, Maaløe, L & Igel, C 2021, On scaling contrastive representations for low-resource speech recognition . in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing . vol. 2021-, IEEE, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings, pp. 3885-3889, 2021 IEEE International Conference on Acoustics, Speech and Signal Processing, Toronto, Ontario, Canada, 06/06/2021 . https://doi.org/10.1109/ICASSP39728.2021.9414310
ICASSP

مصطلحات موضوعية: FOS: Computer and information sciences, Self-supervised learning, Sound (cs.SD), Computer Science - Machine Learning, Computer science, Speech recognition, Automatic speech recognition, Semi-supervised learning, Extension (predicate logic), Parameter space, Unsupervised learning, Representation learning, Computer Science - Sound, Machine Learning (cs.LG), Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, Scaling, Decorrelation, Feature learning, Subspace topology, Electrical Engineering and Systems Science - Audio and Speech Processing

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a291b0d2bde1237368fa4c58d015cbe
http://arxiv.org/abs/2102.00850

المؤلفون: Jan Latko, Joakim Edin, Lorenzo Belgrano, Jakob D. Havtorn, Nicolai F. Jacobsen, Lasse Borgholt, Regitze Sdun, Lars Maaløe, Željko Agić

المصدر: ACL
Havtorn, J D, Latko, J, Edin, J, Borgholt, L, Maaloe, L, Belgrano, L, Jacobsen, N F, Sdun, R & Agic, Z 2020, MultiQT: Multimodal Learning for Real-Time Question Tracking in Speech . in Proceedings of 58 th Annual Meeting of the Association for Computational Linguistics . pp. 2370-2380, 58 th Annual Meeting of the Association for Computational Linguistics, 06/07/2020 .

مصطلحات موضوعية: FOS: Computer and information sciences, Sound (cs.SD), Training set, Computer Science - Computation and Language, Computer science, Speech recognition, 020206 networking & telecommunications, 02 engineering and technology, Sequence labeling, Computer Science - Sound, Multimodal learning, Audio and Speech Processing (eess.AS), 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Transcription (software), Computation and Language (cs.CL), Electrical Engineering and Systems Science - Audio and Speech Processing

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff35772ef17b0a914a191937f2072577

المؤلفون: Dirk Hovy, Peter Simonsen, Lasse Borgholt

المصدر: EMNLP

مصطلحات موضوعية: Training set, Computer science, business.industry, Speech recognition, Sentiment analysis, NATURAL LANGUAGE PROCESSING, Regression analysis, Artificial intelligence, business, computer.software_genre, computer, Natural language processing

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::860fa4ed7e8ca206e32e16c724226858
http://hdl.handle.net/11565/4006591