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arXiv 2023年

作者： Thulke, David Daheim, Nico Dugast, Christian Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany AppTek GmbH Aachen Germany

This paper summarizes our contributions to the document-grounded dialog tasks at the 9th and 10th Dialog System technology Challenges (DSTC9 and DSTC10). In both iterations the task consists of three subtasks: first detect whether the current turn is knowledge seeking, second select a relevant knowledge document, and third generate a response grounded on the selected document. For DSTC9 we proposed different approaches to make the selection task more efficient. The best method, Hierarchical Selection, actually improves the results compared to the original baseline and gives a speedup of 24x. In the DSTC10 iteration of the task, the challenge was to adapt systems trained on written dialogs to perform well on noisy automatic speech recognition transcripts. Therefore, we proposed data augmentation techniques to increase the robustness of the models as well as methods to adapt the style of generated responses to fit well into the proceeding dialog. Additionally, we proposed a noisy channel model that allows for increasing the factuality of the generated responses. In addition to summarizing our previous contributions, in this work, we also report on a few small improvements and reconsider the automatic evaluation metrics for the generation task which have shown a low correlation to human judgments. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Cascaded span extraction and response generation for document-grounded dialog

arXiv

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arXiv 2021年

作者： Daheim, Nico Thulke, David Dugast, Christian Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany AppTek GmbH Aachen Germany

This paper summarizes our entries to both subtasks of the first DialDoc shared task which focuses on the agent response prediction task in goal-oriented document-grounded dialogs. The task is split into two subtasks: predicting a span in a document that grounds an agent turn and generating an agent response based on a dialog and grounding document. In the first subtask, we restrict the set of valid spans to the ones defined in the dataset, use a biaffine classifier to model spans, and finally use an ensemble of different models. For the second subtask, we use a cascaded model which grounds the response prediction on the predicted span instead of the full document. With these approaches, we obtain significant improvements in both subtasks compared to the baseline. Copyright © 2021, The Authors. All rights reserved.

关键词： Forecasting

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IMPROVING FACTORED HYBRID HMM ACOUSTIC MODELING WITHOUT STATE TYING

arXiv

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arXiv 2022年

作者： Raissi, Tina Beck, Eugen Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany AppTek GmbH Aachen Germany

In this work, we show that a factored hybrid hidden Markov model (FH-HMM) which is defined without any phonetic state-tying outperforms a state-of-the-art hybrid HMM. The factored hybrid HMM provides a link to transducer models in the way it models phonetic (label) context while preserving the strict separation of acoustic and language model of the hybrid HMM approach. Furthermore, we show that the factored hybrid model can be trained from scratch without using phonetic state-tying in any of the training steps. Our modeling approach enables triphone context while avoiding phonetic state-tying by a decomposition into locally normalized factored posteriors for monophones/HMM states in phoneme context. Experimental results are provided for Switchboard 300h and LibriSpeech. On the former task we also show that by avoiding the phonetic state-tying step, the factored hybrid can take better advantage of regularization techniques during training, compared to the standard hybrid HMM with phonetic state-tying based on classification and regression trees (CART).MSC Codes 68T10 Copyright © 2022, The Authors. All rights reserved.

关键词： Hidden Markov models

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DEEP HIERARCHICAL BOTTLENECK MRASTA FEATURES FOR LVCSR

DEEP HIERARCHICAL BOTTLENECK MRASTA FEATURES FOR LVCSR

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zoltan Tuske Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)9781479903573

Hierarchical Multi Layer Perceptron (MLP) based long-term feature extraction is optimized for TANDEM connectionist large vocabulary continuous speech recognition (LVCSR) system within the QUAERO project. Training the bottleneck MLP on multi-resolutional RASTA filtered critical band energies, more than 20% relative word error rate (WER) reduction over standard MFCC system is observed after optimizing the number of target labels. Furthermore, introducing a deeper structure in the hierarchical bottleneck processing the relative gain increases to 25%. The final system based on deep bottleneck TANDEM features clearly outperforms the hybrid approach, even if the long-term features are also presented to the deep MLP acoustic model. The results are also verified on evaluation data of the year 2012, and about 20% relative WER improvement over classical cepstral system is measured even after speaker adaptive training.

关键词： LVCSR MRASTA MLP bottleneck hierarchical deep neural network hybrid TANDEM Traffic Bottlenecks CSRP3 gene Hierarchical HYBRID Tandem relative gain

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MULTILINGUAL MRASTA FEATURES FOR LOW-RESOURCE KEYWORD SEARCH AND SPEECH recognition SYSTEMS

MULTILINGUAL MRASTA FEATURES FOR LOW-RESOURCE KEYWORD SEARCH...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zoltan Tuske David Nolden Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)9781479928941

This paper investigates the application of hierarchical MRASTA bottleneck (BN) features for under-resourced languages within the IARPA Babel project. Through multilingual training of Multilayer Perceptron (MLP) BN features on five languages (Cantonese, Pashto, Tagalog, Turkish, and Vietnamese), we could end up in a single feature stream which is more beneficial to all languages than the unilingual features. In the case of balanced corpus sizes, the multilingual BN features improve the automatic speech recognition (ASR) performance by 3-5% and the keyword search (KWS) by 3-10% relative for both limited (LLP) and full language packs (FLP). Borrowing orders of magnitude more data from non-target FLPs, the recognition error rate is reduced by 8-10%, and the spoken term detection is improved by over 40% relative on Vietnamese and Pashto LLP. Aiming at the fast development of acoustic models, cross-lingual transfer of multilingually "pretrained" BN features for a new language is also investigated. Without the need of any MLP training on the new language, the ported BN features performed similarly to the unilingual features on FLP and significantly better on LLP. Results also show that a simple fine-tuning step on the new language is enough to achieve comparable KWS and ASR performance to that system where the target language is also involved in the time-consuming multilingual training.

关键词： language Speech recognition Vietnamese Vietnamese Object oriented programming languages fault-location problem KEYWORD Juvenile Spinal Muscular Atrophy Error analysis CSRP3 gene aquifer storage and recovery

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Sign language Gesture Classification Using Neural Networks 4

Sign Language Gesture Classification Using Neural Networks

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4th International Conference on Advances in Speech and language Technologies for Iberian languages, IberSPEECH 2018

作者： Parcheta, Zuzanna Martínez-Hinarejos, Carlos-D. Sciling S.L. Carrer del Riu 321 Pinedo 46012 Spain Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino de Vera s/n 46022 Spain

Recent studies have demonstrated the power of neural networks for different fields of artificial intelligence. In most fields, such as machine translation or speech recognition, neural networks outperform previously used methods (Hidden Markov Models with Gaussian Mixtures, Statistical Machine Translation, etc.). In this paper, the efficiency of the LeNet convolutional neural network for isolated word sign language recognition is demonstrated. As a preprocessing step, we apply several techniques to obtain the same dimension for the input that contains gesture information. The performance of these preprocessing techniques on a Spanish Sign language dataset is evaluated. These approaches outperform previously obtained results based on Hidden Markov Models. © 4th International Conference, IberSPEECH 2018.

关键词： Gesture recognition

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Comparing the Benefit of Synthetic Training Data for Various Automatic Speech recognition Architectures

Comparing the Benefit of Synthetic Training Data for Various...

引用

IEEE Workshop on Automatic Speech recognition and Understanding

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

ISBN: (纸本)9781665437400

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.

关键词： Training Transducers Conferences Training data Stability analysis Data models Robustness

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Phoneme Based Neural Transducer for Large Vocabulary Speech recognition

Phoneme Based Neural Transducer for Large Vocabulary Speech ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Wei Zhou Simon Berger Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany AppTek GmbH Aachen 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.

关键词： Training Vocabulary Transducers Hidden Markov models Speech recognition Switches Phonetics

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Joshua: An Open Source Toolkit for Parsing-based Machine Translation 4

Joshua: An Open Source Toolkit for Parsing-based Machine Tra...

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4th Workshop on Statistical Machine Translation, WMT 2009, immediately preceding the 12th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2009

作者： Li, Zhifei Callison-Burch, Chris Dyer, Chris Ganitkevitch, Juri Khudanpur, Sanjeev Schwartz, Lane Thornton, Wren N. G. Weese, Jonathan Zaidan, Omar F. Center For Language And Speech Processing Johns Hopkins University BaltimoreMD United States Computational Linguistics And Information Processing Lab University of Maryland College ParkMD United States Human Language Technology And Pattern Recognition Group RWTH Aachen University Germany Natural Language Processing Lab University of Minnesota MinneapolisMN United States

We describe Joshua, an open source toolkit for statistical machine translation. Joshua implements all of the algorithms required for synchronous context free grammars (SCFGs): chart-parsing, ngram language model integration, beamand cube-pruning, and k-best extraction. The toolkit also implements suffix-array grammar extraction and minimum error rate training. It uses parallel and distributed computing techniques for scalability. We demonstrate that the toolkit achieves state of the art translation performance on the WMT09 French-English translation task. ©2009 Association for Computational Linguistics.

关键词： Distributed computer systems

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Articulatory motivated acoustic features for speech recognition

Articulatory motivated acoustic features for speech recognit...

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9th European Conference on Speech Communication and technology

作者： Kocharov, Daniil Zolnay, András Schlüter, Ralf Ney, Hermann Department of Phonetics Faculty of Philology Saint-Petersburg State University 199034 Saint Petersburg Russia Human Language Technology and Pattern Recognition Lehrstuhl für Informatik VI Computer Science Department RWTH Aachen University 52056 Aachen Germany

In this paper, we consider the use of multiple acoustic features of the speech signal for continuous speech recognition. A novel articulatory motivated acoustic feature is introduced, namely the spectrum derivative feature. The new feature is tested in combination with the standard Mel Frequency Cepstral Coefficients (MFCC) and the voicedness features. Linear Discriminant Analysis is applied to find the optimal combination of different acoustic features. Experiments have been performed on small and large vocabulary tasks. Significant improvements in word error rate have been obtained by combining the MFCC feature with the articulatory motivated voicedness and spectrum derivative features: improvements of up to 25% on the small-vocabulary task and improvements of up to 4% on the large-vocabulary task relative to using MFCC alone with the same overall number of parameters in the system.

关键词： Speech recognition

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