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arXiv 2018年

作者： Menne, Tobias Schluter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

This paper presents, in the context of multi-channel ASR, a method to adapt a mask based, statistically optimal beamforming approach to a speaker of interest. The beamforming vector of the statistically optimal beamformer is computed by utilizing speech and noise masks, which are estimated by a neural network. The proposed adaptation approach is based on the integration of the beamformer, which includes the mask estimation network, and the acoustic model of the ASR system. This allows for the propagation of the training error, from the acoustic modeling cost function, all the way through the beamforming operation and through the mask estimation network. By using the results of a first pass recognition and by keeping all other parameters fixed, the mask estimation network can therefore be fine tuned by retraining. Utterances of a speaker of interest can thus be used in a two pass approach, to optimize the beamforming for the speech characteristics of that specific speaker. It is shown that this approach improves the ASR performance of a state-of-the-art multi-channel ASR system on the CHiME-4 data. Furthermore the effect of the adaptation on the estimated speech masks is discussed. Copyright © 2018, The Authors. All rights reserved.

关键词： Cost functions

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Incorporating On-demand Stereo for Real Time recognition

Incorporating On-demand Stereo for Real Time Recognition

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Conference on Computer Vision and pattern recognition (CVPR)

作者： T. Deselaers A. Criminisi J. Winn A. Agarwal Microsoft Research Limited Cambridge UK Human Language Technology and Pattern Recognition RWTH Aachen University of Technology Germany

A new method for localising and recognising hand poses and objects in real-time is presented. This problem is important in vision-driven applications where it is natural for a user to combine hand gestures and real objects when interacting with a machine. Examples include using a real eraser to remove words from a document displayed on an electronic surface. In this paper the task of simultaneously recognising object classes, hand gestures and detecting touch events is cast as a single classification problem. A random forest algorithm is employed which adaptively selects and combines a minimal set of appearance, shape and stereo features to achieve maximum class discrimination for a given image. This minimal set leads to both efficiency at run time and good generalisation. Unlike previous stereo works which explicitly construct disparity maps, here the stereo matching costs are used directly as visual cue and only computed on-demand, i.e. only for pixels where they are necessary for recognition. This leads to improved efficiency. The proposed method is assessed on a database of a variety of objects and hand poses selected for interacting on a flat surface in an office environment.

关键词： Object detection pattern recognition Shape Cameras humans Event detection Costs Visual databases Hardware Object recognition

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Conspiracy vs Critical Thinking Using an Ensemble of Transformers with Data Augmentation Techniques 25

Conspiracy vs Critical Thinking Using an Ensemble of Transfo...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Tulbure, Angelo Maximilian Ardanuy, Mariona Coll Universitat Politècnica de València València Spain Politecnico di Milano Milan Italy Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

This paper provides an overview of our contributions to the PAN at CLEF2024 Oppositional thinking analysis shared task, which focuses on distinguishing between conspiratorial and critical thinking narratives. The competition featured two main tasks. The first task is a binary classification task that aims at determining whether a text is conspiratorial or critical. The second task is a span-level detection task, in which the goal is to detect elements of oppositional narratives in the texts. Two annotated datasets, one in English and one in Spanish, were provided, each of 5K telegram comments. Our best-performing approaches combined custom fine-tuned Transformer models with data augmentation techniques. We achieved an F1-Score of 0.8917 for English and of 0.8293 for Spanish for task 1, and a span-F1 score of 0.6279 for English and 0.6129 for Spanish for task 2. Our task 2 approach achieved the best results in the shared task for both English and Spanish. © 2024 Copyright for this paper by its authors.

关键词： binary classification conspiracy theories critical thinking data augmentation ensembling models oppositional thinking PAN 2024 token classification

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Comparison of lattice-free and lattice-based sequence discriminative training criteria for LVCSR

arXiv

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arXiv 2019年

作者： Michel, Wilfried Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen52056 Germany

Sequence discriminative training criteria have long been a standard tool in automatic speech recognition for improving the performance of acoustic models over their maximum likelihood / cross entropy trained counterparts. While previously a lattice approximation of the search space has been necessary to reduce computational complexity, recently proposed methods use other approximations to dispense of the need for the computationally expensive step of separate lattice creation. In this work we present a memory efficient implementation of the forward-backward computation that allows us to use unigram word-level language models in the denominator calculation while still doing a full summation on GPU. This allows for a direct comparison of lattice-based and lattice-free sequence discriminative training criteria such as MMI and sMBR, both using the same language model during training. We compared performance, speed of convergence, and stability on large vocabulary continuous speech recognition tasks like Switchboard and Quaero. We found that silence modeling seriously impacts the performance in the lattice-free case and needs special treatment. In our experiments lattice-free MMI comes on par with its lattice-based counterpart. Lattice-based sMBR still outperforms all lattice-free training criteria. Copyright © 2019, The Authors. All rights reserved.

关键词： Acoustic Modeling

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A comparative study on end-to-end speech to text translation

arXiv

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arXiv 2019年

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

Recent advances in deep learning show that end-to-end speech to text translation model is a promising approach to direct the speech translation field. In this work, we provide an overview of different end-to-end architectures, as well as the usage of an auxiliary connectionist temporal classification (CTC) loss for better convergence. We also investigate on pre-training variants such as initializing different components of a model using pretrained models, and their impact on the final performance, which gives boosts up to 4% in BLEU and 5% in TER. Our experiments are performed on 270h IWSLT TED-talks En→De, and 100h LibriSpeech Audio-books En→Fr. We also show improvements over the current end-to-end state-of-the-art systems on both tasks. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Automatic semantic segmentation of structural elements related to the spinal cord in the lumbar region by using convolutional neural networks 25

Automatic semantic segmentation of structural elements relat...

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25th International Conference on pattern recognition, ICPR 2020

作者： Saenz-Gamboa, Jhon Jairo de la Iglesia-Vayá, Maria Gómez, Jon A. Biomedical Imaging Joint Unit Foundation for the Promotion of Health and Biomedical Research FISABIO-CIPF València Spain Pattern Recognition and Human Language Technology research center Universitat Politècnica de València València Spain

ISBN: (纸本)9781728188089

This work addresses the problem of automatically segmenting the MR images corresponding to the lumbar spine. The purpose is to detect and delimit the different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, etc. This task is known as semantic segmentation. The approach proposed in this work is based on convolutional neural networks whose output is a mask where each pixel from the input image is classified into one of the possible classes. Classes were defined by radiologists and correspond to structural elements and tissues. The proposed network architectures are variants of the U-Net. Several complementary blocks were used to define the variants: spatial attention models, deep supervision and multi-kernels at input, this last block type is based on the idea of inception. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline. © 2020 IEEE

关键词： Semantic Segmentation

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A Comparison of Transformer and LSTM Encoder Decoder Models for ASR

A Comparison of Transformer and LSTM Encoder Decoder Models ...

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IEEE Workshop on Automatic Speech recognition and Understanding

作者： Albert Zeyer Parnia Bahar Kazuki Irie Ralf Schlüter Hermann Ney AppTek GmbH Aachen Germany Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

ISBN: (数字)9781728103068

ISBN: (纸本)9781728103075

We present competitive results using a Transformer encoder-decoder-attention model for end-to-end speech recognition needing less training time compared to a similarly performing LSTM model. We observe that the Transformer training is in general more stable compared to the LSTM, although it also seems to overfit more, and thus shows more problems with generalization. We also find that two initial LSTM layers in the Transformer encoder provide a much better positional encoding. Data-augmentation, a variant of SpecAugment, helps to improve both the Transformer by 33% and the LSTM by 15% relative. We analyze several pretraining and scheduling schemes, which is crucial for both the Transformer and the LSTM models. We improve our LSTM model by additional convolutional layers. We perform our experiments on Lib-riSpeech 1000h, Switchboard 300h and TED-LIUM-v2 200h, and we show state-of-the-art performance on TED-LIUM-v2 for attention based end-to-end models. We deliberately limit the training on LibriSpeech to 12.5 epochs of the training data for comparisons, to keep the results of practical interest, although we show that longer training time still improves more. We publish all the code and setups to run our experiments.

关键词： Decoding Hidden Markov models Training Convergence Encoding Convolutional codes Data models

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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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ACOUSTIC MODELING OF SPEECH WAVEFORM BASED ON MULTI-RESOLUTION, NEURAL NETWORK SIGNAL PROCESSING

ACOUSTIC MODELING OF SPEECH WAVEFORM BASED ON MULTI-RESOLUTI...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zoltán Tüske Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)9781538646595

Recently, several papers have demonstrated that neural networks (NN) are able to perform the feature extraction as part of the acoustic model. Motivated by the Gammatone feature extraction pipeline, in this paper we extend the waveform based NN model by a second level of time-convolutional element. The proposed extension generalizes the envelope extraction block, and allows the model to learn multi-resolutional representations. Automatic speech recognition (ASR) experiments show significant word error rate reduction over our previous best acoustic model trained in the signal domain directly. Although we use only 250 hours of speech, the data-driven NN based speech signal processing performs nearly equally to traditional handcrafted feature extractors. In additional experiments, we also test segment-level feature normalization techniques on NN derived features, which improve the results further. However, the porting of speech representations derived by a feed-forward NN to a LSTM back-end model indicates much less robustness of the NN front-end compared to the standard feature extractors. Analysis of the weights in the proposed new layer reveals that the NN prefers both multi-resolution and modulation spectrum representations.

关键词： ASR neural network time-signal waveform modulation spectrum feature representation modulation spectra waveforms time signal Speech recognition Neural network aquifer storage and recovery Feature extraction Speech signal processing Multi-resolution Acoustic 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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