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检索条件"机构=Human Language Technology And Pattern Recognition Group"
397 条 记 录,以下是101-110 订阅
排序:
Phoneme based neural transducer for large vocabulary speech recognition
arXiv
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arXiv 2020年
作者: Zhou, Wei Berger, Simon Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany
To join the advantages of classical and end-to-end approaches for speech recognition, we present a simple, novel and competitive approach for phoneme-based neural transducer modeling. Different alignment label topolog... 详细信息
来源: 评论
FULL-SUM DECODING FOR HYBRID HMM BASED SPEECH recognition USING LSTM language MODEL
arXiv
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arXiv 2020年
作者: Zhou, Wei Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany
In hybrid HMM based speech recognition, LSTM language models have been widely applied and achieved large improvements. The theoretical capability of modeling any unlimited context suggests that no recombination should... 详细信息
来源: 评论
Early stage LM integration using local and global log-linear combination
arXiv
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arXiv 2020年
作者: Michel, Wilfried Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52056 Germany AppTek GmbH Aachen52062 Germany
Sequence-to-sequence models with an implicit alignment mechanism (e.g. attention) are closing the performance gap towards traditional hybrid hidden Markov models (HMM) for the task of automatic speech recognition. One... 详细信息
来源: 评论
A new training pipeline for an improved neural transducer
arXiv
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arXiv 2020年
作者: Zeyer, Albert Merboldt, André Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52062 Germany AppTek GmbH Aachen52062 Germany
The RNN transducer is a promising end-to-end model candidate. We compare the original training criterion with the full marginalization over all alignments, to the commonly used maximum approximation, which simplifies,... 详细信息
来源: 评论
The rwth asr system for ted-lium release 2: improving hybrid hmm with specaugment
arXiv
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arXiv 2020年
作者: Zhou, Wei Michel, Wilfried Irie, Kazuki Kitza, Markus Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany
We present a complete training pipeline to build a state-of-the-art hybrid HMM-based ASR system on the 2nd release of the TED-LIUM corpus. Data augmentation using SpecAugment is successfully applied to improve perform... 详细信息
来源: 评论
Robust Beam Search for Encoder-Decoder Attention Based Speech recognition without Length Bias
arXiv
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arXiv 2020年
作者: Zhou, Wei Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany
As one popular modeling approach for end-to-end speech recognition, attention-based encoder-decoder models are known to suffer the length bias and corresponding beam problem. Different approaches have been applied in ... 详细信息
来源: 评论
A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models
arXiv
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arXiv 2020年
作者: Zeineldeen, Mohammad Zeyer, Albert Zhou, Wei Ng, Thomas Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52062 Aachen Germany AppTek GmbH Aachen52062 Germany
Following the rationale of end-to-end modeling, CTC, RNN-T or encoder-decoder-attention models for automatic speech recognition (ASR) use graphemes or grapheme-based subword units based on e.g. byte-pair encoding (BPE... 详细信息
来源: 评论
Improving unsupervised word-by-word translation with language model and denoising autoencoder
arXiv
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arXiv 2019年
作者: Kim, Yunsu Geng, Jiahui Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany
Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods... 详细信息
来源: 评论
Generalizing back-translation in neural machine translation
arXiv
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arXiv 2019年
作者: Graca, Miguel Kim, Yunsu Schamper, Julian Khadivi, Shahram Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany
Back-translation - data augmentation by translating target monolingual data - is a crucial component in modern neural machine translation (NMT). In this work, we reformulate back-translation in the scope of crossentro... 详细信息
来源: 评论
When and why is document-level context useful in neural machine translation?
arXiv
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arXiv 2019年
作者: Duc, Yunsu Kim Tran, Thanh Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany
Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences. However, recent advances in document-level NMT focus on sophisticated integration of the c... 详细信息
来源: 评论