检索结果-内蒙古大学图书馆

MORPHEME-BASED FEATURE-RICH language MODELS USING DEEP NEURAL NETWORKS FOR LVCSR OF EGYPTIAN ARABIC

学校读者我要写书评

暂无评论

MORPHEME-BASED FEATURE-RICH LANGUAGE MODELS USING DEEP NEURA...

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Amr El-Desoky Mousa Hong-Kwang Jeff Kuo Lidia Mangu Hagen Soltau Human Language Technology and Pattern Recognition - Computer Science Department RWTH Aachen University IBM T. J. Watson Research Center

ISBN: (纸本)9781479903573

Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech recognition (LVCSR). Building LMs on morpheme level is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs.

关键词： language model Morpheme Feature-rich Deep neural network Egyptian Arabic modelling languages Neural network Arabic language additional features Word Religious Missions Continuous speech recognition

Equivalence of segmental and neural transducer modeling: A proof of concept

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zhou, Wei Zeyer, Albert Merboldt, André Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

With the advent of direct models in automatic speech recognition (ASR), the formerly prevalent frame-wise acoustic modeling based on hidden Markov models (HMM) diversified into a number of modeling architectures like encoder-decoder attention models, transducer models and segmental models (direct HMM). While transducer models stay with a frame-level model definition, segmental models are defined on the level of label segments directly. While (soft-)attention-based models avoid explicit alignment, transducer and segmental approach internally do model alignment, either by segment hypotheses or, more implicitly, by emitting so-called blank symbols. In this work, we prove that the widely used class of RNN-Transducer models and segmental models (direct HMM) are equivalent and therefore show equal modeling power. It is shown that blank probabilities translate into segment length probabilities and vice versa. In addition, we provide initial experiments investigating decoding and beam-pruning, comparing time-synchronous and label-/segment-synchronous search strategies and their properties using the same underlying model. Copyright © 2021, The Authors. All rights reserved.

关键词： Transducers

Cumulative adaptation for BLSTM acoustic models

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Kitza, Markus Golik, Pavel Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

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 temporal relationships and translation invariant representations, is used for robust acoustic modeling. Further, i-vectors were used as an input to the neural network to perform instantaneous speaker and environment adaptation, providing 8% relative improvement in word error rate on the NIST Hub5 2000 evaluation testset. By enhancing the first-pass i-vector based adaptation with a second-pass adaptation using speaker and environment dependent transformations within the network, a further relative improvement of 5% in word error rate was achieved. We have reevaluated the features used to estimate i-vectors and their normalization to achieve the best performance in a modern large scale automatic speech recognition system. Copyright © 2019, The Authors. All rights reserved.

关键词： Speech recognition

CONFORMER-BASED HYBRID ASR SYSTEM FOR SWITCHBOARD DATASET

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Zeineldeen, Mohammad Xu, Jingjing Lüscher, Christoph Michel, Wilfried Gerstenberger, Alexander Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

The recently proposed conformer architecture has been successfully used for end-to-end automatic speech recognition (ASR) architectures achieving state-of-the-art performance on different datasets. To our best knowledge, the impact of using conformer acoustic model for hybrid ASR is not investigated. In this paper, we present and evaluate a competitive conformer-based hybrid model training recipe. We study different training aspects and methods to improve word-error-rate as well as to increase training speed. We apply time downsampling methods for efficient training and use transposed convolutions to upsample the output sequence again. We conduct experiments on Switchboard 300h dataset and our conformer-based hybrid model achieves competitive results compared to other architectures. It generalizes very well on Hub5'01 test set and outperforms the BLSTM-based hybrid model significantly. Copyright © 2021, The Authors. All rights reserved.

关键词： Speech recognition

Improved training of end-to-end attention models for speech recognition

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Zeyer, Albert Irie, Kazuki Schlüter, Ralf Ney, Hermann Computer Science Department Rwth Aachen University Human Language Technology and Pattern Recognition Aachen52062 Germany AppTek United States Nnaisense Switzerland

Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition. In this work, we show that such models can achieve competitive results on the Switchboard 300h and LibriSpeech 1000h tasks. In particular, we report the state-of-the-art word error rates (WER) of 3.54% on the dev-clean and 3.82% on the testclean evaluation subsets of LibriSpeech. We introduce a new pretraining scheme by starting with a high time reduction factor and lowering it during training, which is crucial both for convergence and final performance. In some experiments, we also use an auxiliary CTC loss function to help the convergence. In addition, we train long short-term memory (LSTM) language models on subword units. By shallow fusion, we report up to 27% relative improvements in WER over the attention baseline without a language model. Copyright © 2018, The Authors. All rights reserved.

关键词： Long short-term memory

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

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

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

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 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

Comparing the benefit of synthetic training data for various automatic speech recognition architectures

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Rossenbach, Nick Zeineldeen, Mohammad Hilmes, Benedikt Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52074 Germany AppTek GmbH Aachen52062 Germany

Recent publications on automatic-speech-recognition (ASR) have a strong focus on attention encoder-decoder (AED) architectures which tend to suffer from over-fitting in low resource scenarios. One solution to tackle this issue is to generate synthetic data with a trained text-to-speech system (TTS) if additional text is available. This was successfully applied in many publications with AED systems, but only very limited in the context of other ASR architectures. We investigate the effect of varying pre-processing, the speaker embedding and input encoding of the TTS system w.r.t. the effectiveness of the synthesized data for AED-ASR training. Additionally, we also consider internal language model subtraction for the first time, resulting in up to 38% relative improvement. We compare the AED results to a state-of-the-art hybrid ASR system, a monophone based system using connectionist-temporal-classification (CTC) and a monotonic transducer based system. We show that for the later systems the addition of synthetic data has no relevant effect, but they still outperform the AED systems on LibriSpeech-100h. We achieve a final word-error-rate of 3.3%/10.0% with a hybrid system on the clean/noisy test-sets, surpassing any previous state-of-the-art systems on Librispeech-100h that do not include unlabeled audio data. Copyright © 2021, The Authors. All rights reserved.

关键词： Speech recognition