检索结果-内蒙古大学图书馆

Two-way neural machine translation: A proof of concept for bidirectional translation modeling using a two-dimensional grid

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Bahar, Parnia Brix, Christopher Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

Neural translation models have proven to be effective in capturing sufficient information from a source sentence and generating a high-quality target sentence. However, it is not easy to get the best effect for bidirectional translation, i.e., both source-to-target and target-to-source translation using a single model. If we exclude some pioneering attempts, such as multilingual systems, all other bidirectional translation approaches are required to train two individual models. This paper proposes to build a single end-to-end bidirectional translation model using a two-dimensional grid, where the left-to-right decoding generates source-to-target, and the bottom-to-up decoding creates target-to-source output. Instead of training two models independently, our approach encourages a single network to jointly learn to translate in both directions. Experiments on the WMT 2018 German↔English and Turkish↔English translation tasks show that the proposed model is capable of generating a good translation quality and has sufficient potential to direct the research. © 2020, CC-BY.

关键词： Decoding

Tight integrated end-to-end training for cascaded speech translation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Bahar, Parnia Bieschke, Tobias Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

A cascaded speech translation model relies on discrete and non-differentiable transcription, which provides a supervision signal from the source side and helps the transformation between source speech and target text. Such modeling suffers from error propagation between ASR and MT models. Direct speech translation is an alternative method to avoid error propagation;however, its performance is often behind the cascade system. To use an intermediate representation and preserve the end-to-end trainability, previous studies have proposed using two-stage models by passing the hidden vectors of the recognizer into the decoder of the MT model and ignoring the MT encoder. This work explores the feasibility of collapsing the entire cascade components into a single end-to-end trainable model by optimizing all parameters of ASR and MT models jointly without ignoring any learned parameters. It is a tightly integrated method that passes renormalized source word posterior distributions as a soft decision instead of one-hot vectors and enables backpropagation. Therefore, it provides both transcriptions and translations and achieves strong consistency between them. Our experiments on four tasks with different data scenarios show that the model outperforms cascade models up to 1.8% in BLEU and 2.0% in TER and is superior compared to direct models. © 2020, CC-BY.

关键词： Backpropagation

Phoneme based neural transducer for large vocabulary speech recognition

学校读者我要写书评

暂无评论

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 topologies are compared and word-end-based phoneme label augmentation is proposed to improve performance. Utilizing the local dependency of phonemes, we adopt a simplified neural network structure and a straightforward integration with the external word-level language model to preserve the consistency of seq-to-seq modeling. We also present a simple, stable and efficient training procedure using frame-wise cross-entropy loss. A phonetic context size of one is shown to be sufficient for the best performance. A simplified scheduled sampling approach is applied for further improvement and different decoding approaches are briefly compared. The overall performance of our best model is comparable to state-of-the-art (SOTA) results for the TED-LIUM Release 2 and Switchboard corpora. Copyright © 2020, The Authors. All rights reserved.

关键词： Transducers

FULL-SUM DECODING FOR HYBRID HMM BASED SPEECH recognition USING LSTM language MODEL

学校读者我要写书评

暂无评论

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 be applied in decoding. This motivates to reconsider full summation over the HMM-state sequences instead of Viterbi approximation in decoding. We explore the potential gain from more accurate probabilities in terms of decision making and apply the full-sum decoding with a modified prefix-tree search framework. The proposed full-sum decoding is evaluated on both Switchboard and Librispeech corpora. Different models using CE and sMBR training criteria are used. Additionally, both MAP and confusion network decoding as approximated variants of general Bayes decision rule are evaluated. Consistent improvements over strong baselines are achieved in almost all cases without extra cost. We also discuss tuning effort, efficiency and some limitations of full-sum decoding. Copyright © 2020, The Authors. All rights reserved.

关键词： Decoding

Early stage LM integration using local and global log-linear combination

学校读者我要写书评

暂无评论

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 important factor to improve word error rate in both cases is the use of an external language model (LM) trained on large text-only corpora. language model integration is straightforward with the clear separation of acoustic model and language model in classical HMM-based modeling. In contrast, multiple integration schemes have been proposed for attention models. In this work, we present a novel method for language model integration into implicit-alignment based sequence-to-sequence models. Log-linear model combination of acoustic and language model is performed with a per-token renormalization. This allows us to compute the full normalization term efficiently both in training and in testing. This is compared to a global renormalization scheme which is equivalent to applying shallow fusion in training. The proposed methods show good improvements over standard model combination (shallow fusion) on our state-of-the-art Librispeech system. Furthermore, the improvements are persistent even if the LM is exchanged for a more powerful one after training. Copyright © 2020, The Authors. All rights reserved.

关键词： Hidden Markov models

A new training pipeline for an improved neural transducer

学校读者我要写书评

暂无评论

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, improves and speeds up our training. We also generalize from the original neural network model and study more powerful models, made possible due to the maximum approximation. We further generalize the output label topology to cover RNN-T, RNA and CTC. We perform several studies among all these aspects, including a study on the effect of external alignments. We find that the transducer model generalizes much better on longer sequences than the attention model. Our final transducer model outperforms our attention model on Switchboard 300h by over 6% relative WER. Copyright © 2020, The Authors. All rights reserved.

关键词： RNA

Robust Beam Search for Encoder-Decoder Attention Based Speech recognition without Length Bias

学校读者我要写书评

暂无评论

arXiv 2020年

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 simple beam search to ease the problem, most of which are heuristic-based and require considerable tuning. We show that heuristics are not proper modeling refinement, which results in severe performance degradation with largely increased beam sizes. We propose a novel beam search derived from reinterpreting the sequence posterior with an explicit length modeling. By applying the reinterpreted probability together with beam pruning, the obtained final probability leads to a robust model modification, which allows reliable comparison among output sequences of different lengths. Experimental verification on the LibriSpeech corpus shows that the proposed approach solves the length bias problem without heuristics or additional tuning effort. It provides robust decision making and consistently good performance under both small and very large beam sizes. Compared with the best results of the heuristic baseline, the proposed approach achieves the same WER on the 'clean' sets and 4% relative improvement on the 'other' sets. We also show that it is more efficient with the additional derived early stopping criterion. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models

学校读者我要写书评

暂无评论

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). The mapping from pronunciation to spelling is learned completely from data. In contrast to this, classical approaches to ASR employ secondary knowledge sources in the form of phoneme lists to define phonetic output labels and pronunciation lexica. In this work, we do a systematic comparison between grapheme- and phoneme-based output labels for an encoder-decoder-attention ASR model. We investigate the use of single phonemes as well as BPE-based phoneme groups as output labels of our model. To preserve a simplified and efficient decoder design, we also extend the phoneme set by auxiliary units to be able to distinguish homophones. Experiments performed on the Switchboard 300h and LibriSpeech benchmarks show that phoneme-based modeling is competitive to grapheme-based encoder-decoder-attention modeling. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition

The rwth asr system for ted-lium release 2: improving hybrid hmm with specaugment

学校读者我要写书评

暂无评论

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 performance on top of our best SAT model using i-vectors. By investigating the effect of different maskings, we achieve improvements from SpecAugment on hybrid HMM models without increasing model size and training time. A subsequent sMBR training is applied to fine-tune the final acoustic model, and both LSTM and Transformer language models are trained and evaluated. Our best system achieves a 5.6% WER on the test set, which outperforms the previous state-of-the-art by 27% relative. Copyright © 2020, The Authors. All rights reserved.

关键词： Speech recognition