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Joris Pelemans
Joris Pelemans
Doctoral Researcher in Speech Recognition, KU Leuven
Adresse e-mail validée de esat.kuleuven.be
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Character-word LSTM language models
L Verwimp, J Pelemans, P Wambacq
arXiv preprint arXiv:1704.02813, 2017
632017
Sparse non-negative matrix language modeling for skip-grams
N Shazeer, J Pelemans, C Chelba
Proceedings Interspeech 2015 2015, 1428-1432, 2015
322015
Skip-gram language modeling using sparse non-negative matrix probability estimation
N Shazeer, J Pelemans, C Chelba
arXiv preprint arXiv:1412.1454, 2014
152014
Automatic assessment of children's reading with the FLaVoR decoding using a phone confusion model
E Yilmaz, J Pelemans
Proceedings Interspeech 2014, 969-972, 2014
142014
Analyzing the contribution of top-down lexical and bottom-up acoustic cues in the detection of sentence prominence
S Kakouros, J Pelemans, L Verwimp, P Wambacq, O Räsänen
Proceedings Interspeech 2016 8, 1074-1078, 2016
112016
Improving the translation environment for professional translators
V Vandeghinste, T Vanallemeersch, L Augustinus, B Bulté, F Van Eynde, ...
Informatics 6 (2), 24, 2019
102019
A comparison of different punctuation prediction approaches in a translation context
V Vandeghinste, L Verwimp, J Pelemans, P Wambacq
Proceedings of the 21st Annual Conference of the European Association for …, 2018
102018
SCATE-Smart Computer Aided Translation Environment.
V Vandeghinste, T Vanallemeersch, L Augustinus, J Pelemans, ...
Baltic Journal of Modern Computing 4 (2), 2016
82016
Sparse non-negative matrix language modeling
J Pelemans, N Shazeer, C Chelba
Transactions of the Association for Computational Linguistics 4, 329-342, 2016
72016
Pruning sparse non-negative matrix n-gram language models
J Pelemans, N Shazeer, C Chelba
Proceedings Interspeech 2015 2015, 1433-1437, 2015
72015
Coping with language data sparsity: Semantic head mapping of compound words
J Pelemans, K Demuynck, P Wambacq
2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014
62014
Efficient language model adaptation for automatic speech recognition of spoken translations
J Pelemans, T Vanallemeersch, K Demuynck, P Wambacq
Proceedings Interspeech 2015 2015, 2262-2266, 2015
52015
A layered approach for dutch large vocabulary continuous speech recognition
J Pelemans, K Demuynck, P Wambacq
2012 IEEE international conference on acoustics, speech and signal …, 2012
52012
Van hamme, H., and Wambacq, P.(2019)
L Verwimp, J Pelemans
Tf-lm: Tensorflow-based language modeling toolkit. In http://www. lrec-conf …, 0
5
Language model adaptation for ASR of spoken translations using phrase-based translation models and named entity models
J Pelemans, T Vanallemeersch, K Demuynck, L Verwimp, P Wambacq
2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016
42016
STON efficient subtitling in Dutch using state-of-the-art tools
L Verwimp, B Desplanques, K Demuynck, J Pelemans, M Lycke, ...
17th Annual Conference of the International-Speech-Communication-Association …, 2016
42016
Integrating meta-information into recurrent neural network language models
Y Shi, M Larson, J Pelemans, CM Jonker, P Wambacq, P Wiggers, ...
Speech Communication 73, 64-80, 2015
42015
Dutch automatic speech recognition on the web: Towards a general purpose system
J Pelemans, K Demuynck, P Wambacq
Proceedings Interspeech 2012 3, 2121-2124, 2012
42012
User-initiated repetition-based recovery in multi-utterance dialogue systems
HL Nguyen, V Renkens, J Pelemans, SP Potharaju, AK Nalamalapu, ...
arXiv preprint arXiv:2108.01208, 2021
32021
Information-weighted neural cache language models for asr
L Verwimp, J Pelemans, P Wambacq
2018 IEEE Spoken Language Technology Workshop (SLT), 756-762, 2018
32018
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