Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Pavel Golik"'
Publikováno v:
Proceedings of the 1st Workshop on Benchmarking: Past, Present and Future.
The applications of automatic speech recognition (ASR) systems are proliferating, in part due to recent significant quality improvements. However, as recent work indicates, even state-of-the-art speech recognition systems – some which deliver impre
Publikováno v:
IWSLT
In simultaneous machine translation, the objective is to determine when to produce a partial translation given a continuous stream of source words, with a trade-off between latency and quality. We propose a neural machine translation (NMT) model that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8086838ec56ff26eed99507ea05b3e4f
http://arxiv.org/abs/2005.14489
http://arxiv.org/abs/2005.14489
Publikováno v:
ICASSP
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Despite advances in neural language modeling, obtaining a good model on a large scale multi-domain dataset still remains a difficult task. We propose training methods for building neural language models for such a task, which are not only domain robu
Autor:
Tamer Alkhouli, Andreas Guta, Parnia Bahar, Christian Herold, Evgeny Matusov, Pavel Golik, Patrick Wilken
Publikováno v:
IWSLT
AppTek and RWTH Aachen University team together to participate in the offline and simultaneous speech translation tracks of IWSLT 2020. For the offline task, we create both cascaded and end-to-end speech translation systems, paying attention to caref
Publikováno v:
INTERSPEECH
Interspeech 2019
Interspeech 2019
This paper addresses the robust speech recognition problem as an adaptation task. Specifically, we investigate the cumulative application of adaptation methods. A bidirectional Long Short-Term Memory (BLSTM) based neural network, capable of learning
Autor:
Haipeng Wang, Xiaodong Cui, Ralf Schlüter, Mark J. F. Gales, Bhuvana Ramabhadran, Lidia Mangu, Ellen Kislal, Kate Knill, Anton Ragni, Michael Picheny, P.C. Woodland, Markus Nussbaum-Thom, Hermann Ney, Pavel Golik, Abhinav Sethy, Jia Cui, Zoltán Tüske, Brian Kingsbury, Kartik Audhkhasi
Publikováno v:
ASRU
© 2015 IEEE. This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67a1426b96ba1ac78cc820d5d41e71ca
Autor:
Adria A. Martinez-Villaronga, Adrià Giménez, Hermann Ney, Javier Jorge, Patrick Doetsch, Albert Sanchis, Pavel Golik, Vicent Andreu Císcar, Joan Albert Silvestre-Cerdà, Alfons Juan
Publikováno v:
IberSPEECH 2018
IberSPEECH
IberSPEECH
Publikováno v:
ICASSP
In this paper, we present an investigation on technical details of the byte-level convolutional layer which replaces the conventional linear word projection layer in the neural language model. In particular, we discuss and compare the effective filte
Publikováno v:
Speech and Computer ISBN: 9783319664286
SPECOM
SPECOM
In this paper we describe the RWTH Aachen keyword search (KWS) system developed in the course of the IARPA Babel program. We put focus on acoustic modeling with neural networks and evaluate the full pipeline with respect to the KWS performance. At th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e41be3a13539f4f33aa72a7e01100c1
https://doi.org/10.1007/978-3-319-66429-3_72
https://doi.org/10.1007/978-3-319-66429-3_72
Publikováno v:
ASRU
In the tandem approach, the output of a neural network (NN) serves as input features to a Gaussian mixture model (GMM) aiming to improve the emission probability estimates. As has been shown in our previous work, GMM with pooled covariance matrix can