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arXiv 2019年

作者： Hanselmann, Harald Ney, Hermann Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

The task of fine-grained visual classification (FGVC) deals with classification problems that display a small inter-class variance such as distinguishing between different bird species or car models. State-of-the-art approaches typically tackle this problem by integrating an elaborate attention mechanism or (part-) localization method into a standard convolutional neural network (CNN). Also in this work the aim is to enhance the performance of a backbone CNN such as ResNet by including three efficient and lightweight components specifically designed for FGVC. This is achieved by using global k-max pooling, a discriminative embedding layer trained by optimizing class means and an efficient bounding box estimator that only needs class labels for training. The resulting model achieves new best state-of-the-art recognition accuracies on the Stanford cars and FGVC-Aircraft datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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Fine-Grained Visual Categorization with 2D-Warping

Fine-Grained Visual Categorization with 2D-Warping

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International Conference on pattern recognition

作者： Harald Hanselmann Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

The task of fine-grained visual categorization is related to both general object recognition and specialized tasks such as face recognition. Hence, we propose to combine two methods popular for general object recognition and face recognition to build a new model-free system for fine-grained visual categorization. Specifically, we use Local Naive-Bayes Nearest Neighbor as a pre-selection method and 2D-Warping as a refinement step. For the latter, we explore different ways to use the alignments computed by a 2D-Warping algorithm for classification. We demonstrate the performance of our approach on the CUB200-2011 database and show that our approach outperforms the recognition accuracy of current state-of-the-art methods.

关键词： Training Accuracy Visualization Face recognition Databases Birds Feature extraction

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Analysis of Preprocessing Techniques for Latin Handwriting recognition

Analysis of Preprocessing Techniques for Latin Handwriting R...

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International Workshop on Frontiers in Handwriting recognition

作者： Hendrik Pesch Mahdi Hamdani Jens Forster Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

In this work we analyze the contribution of preprocessing steps for Latin handwriting recognition. A preprocessing pipeline based on geometric heuristics and image statistics is used. This pipeline is applied to French and English handwriting recognition in an HMM based framework. Results show that preprocessing improves recognition performance for the two tasks. The Maximum Likelihood (ML)-trained HMM system reaches a competitive WER of 16.7% and outperforms many sophisticated systems for the French handwriting recognition task. The results for English handwriting are comparable to other ML-trained HMM recognizers. Using MLP preprocessing a WER of 35.3% is achieved.

关键词： Hidden Markov models Databases Handwriting recognition Pipelines Text recognition Noise

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Multilingual Off-Line Handwriting recognition in Real-World Images

Multilingual Off-Line Handwriting Recognition in Real-World ...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Michal Kozielski Patrick Doetsch Mahdi Hamdani Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany

We propose a state-of-the-art system for recognizing real-world handwritten images exposing a huge degree of noise and a high out-of-vocabulary rate. We describe methods for successful image demising, line removal, deskewing, deslanting, and text line segmentation. We demonstrate how to use a HMM-based recognition system to obtain competitive results, and how to further improve it using LSTM neural networks in the tandem approach. The final system outperforms other approaches on a new dataset for English and French handwriting. The presented framework scales well across other standard datasets.

关键词： Hidden Markov models Handwriting recognition Standards Training Image segmentation Vocabulary Algorithm design and analysis

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Chunk-level reordering of source language sentences with automatically learned rules for statistical machine translation

Chunk-level reordering of source language sentences with aut...

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2007 AMTA Workshop on Syntax and Structure in Statistical Translation, SSST 2007 at the 2007 Conference of the North American Chapter of the Association for Computational Linguistics: human language Technologies, NAACL-HLT 2007

作者： Zhang, Yuqi Zens, Richard Ney, Hermann Human Language Technology and Pattern Recognition Lehrstuhl für Informatik 6 Computer Science Department RWTH Aachen University AachenD-52056 Germany

In this paper, we describe a source-side reordering method based on syntactic chunks for phrase-based statistical machine translation. First, we shallow parse the source language sentences. Then, reordering rules are automatically learned from source-side chunks and word alignments. During translation, the rules are used to generate a reordering lattice for each sentence. Experimental results are reported for a Chinese-to-English task, showing an improvement of 0.5%-1.8% BLEU score absolute on various test sets and better computational efficiency than reordering during decoding. The experiments also show that the reordering at the chunk-level performs better than at the POS-level. © 2007 Association for Computational Linguistics

关键词： Computational efficiency

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Impact of automatic segmentation on the quality, productivity and self-reported post-editing effort of intralingual subtitles 10

Impact of automatic segmentation on the quality, productivit...

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10th International Conference on language Resources and Evaluation, LREC 2016

作者： Álvarez, Aitor Balenciaga, Marina Del Pozo, Arantza Arzelus, Haritz Matamala, Anna Martínez-Hinarejos, Carlos-D. Human Speech and Language Technology Group Vicomtech-IK4 San-Sebastian Spain Department of Translation Interpreting and East Asian Studies UAB Barcelona Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Spain

ISBN: (纸本)9782951740891

This paper describes the evaluation methodology followed to measure the impact of using a machine learning algorithm to automatically segment intralingual subtitles. The segmentation quality, productivity and self-reported post-editing effort achieved with such approach are shown to improve those obtained by the technique based in counting characters, mainly employed for automatic subtitle segmentation currently. The corpus used to train and test the proposed automated segmentation method is also described and shared with the community, in order to foster further research in this area.

关键词： Productivity

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Bi-modal handwritten text recognition (BiHTR) ICPR 2010 contest report

Bi-modal handwritten text recognition (BiHTR) ICPR 2010 cont...

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20th International Conference on pattern recognition, ICPR 2010

作者： Pastor, Moisés Paredes, Roberto Pattern Recognition and Human Language Technologies group Department of Information Systems and Computation Technical University of Valencia Cami de Vera s/n. Valencia46022 Spain

ISBN: (纸本)9783642177101

Handwritten text is generally captured through two main modalities: off-line and on-line. Each modality has advantages and disadvantages, but it seems clear that smart approaches to handwritten text recognition (HTR) should make use of both modalities in order to take advantage of the positive aspects of each one. A particularly interesting case where the need of this bi-modal processing arises is when an off-line text, written by some writer, is considered along with the on-line modality of the same text written by another writer. This happens, for example, in computer-assisted transcription of old documents, where on-line text can be used to interactively correct errors made by a main off-line HTR system. In order to develop adequate techniques to deal with this challenging bi-modal HTR recognition task, a suitable corpus is needed. We have collected such a corpus using data (word segments) from the publicly available off-line and on-line IAM data sets. In order to provide the Community with an useful corpus to make easy tests, and to establish baseline performance figures, we have proposed this handwritten bi-modal contest. Here is reported the results of the contest with two participants, one of them achieved a 0% classification error rate, whilst the other participant achieved an interesting 1.5%. © 2010, Springer-Verlag Berlin Heidelberg.

关键词： Character recognition

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Robust Knowledge Distillation from RNN-T Models with Noisy Training Labels Using Full-Sum Loss 48

Robust Knowledge Distillation from RNN-T Models with Noisy T...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zeineldeen, Mohammad Audhkhasi, Kartik Baskar, Murali Karthick Ramabhadran, Bhuvana Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany Google Llc New York United States

ISBN: (纸本)9781728163277

This work studies knowledge distillation (KD) and addresses its constraints for recurrent neural network transducer (RNN-T) models. In hard distillation, a teacher model transcribes large amounts of unlabelled speech to train a student model. Soft distillation is another popular KD method that distills the output logits of the teacher model. Due to the nature of RNN-T alignments, applying soft distillation between RNNT architectures having different posterior distributions is challenging. In addition, bad teachers having high word-error-rate (WER) reduce the efficacy of KD. We investigate how to effectively distill knowledge from variable quality ASR teachers, which has not been studied before to the best of our knowledge. We show that a sequence-level KD, full-sum distillation, outperforms other distillation methods for RNN-T models, especially for bad teachers. We also propose a variant of full-sum distillation that distills the sequence discriminative knowledge of the teacher leading to further improvement in WER. We conduct experiments on public datasets namely SpeechStew and LibriSpeech, and on in-house production data. © 2023 IEEE.

关键词： Recurrent neural networks

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Effective cross-lingual transfer of neural machine translation models without shared vocabularies

arXiv

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arXiv 2019年

作者： Kim, Yunsu Gao, Yingbo Ney, Hermann Human Language Technology and Pattern Recognition Group Rwth Aachen University Aachen Germany

Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies. This paper shows effective techniques to transfer a pre-trained NMT model to a new, unrelated language without shared vocabularies. We relieve the vocabulary mismatch by using cross-lingual word embedding, train a more language-agnostic encoder by injecting artificial noises, and generate synthetic data easily from the pre-training data without back-translation. Our methods do not require restructuring the vocabulary or retraining the model. We improve plain NMT transfer by up to +5.1% BLEU in five low-resource translation tasks, outperforming multilingual joint training by a large margin. We also provide extensive ablation studies on pre-trained embedding, synthetic data, vocabulary size, and parameter freezing for a better understanding of NMT transfer. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural machine translation

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Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under mismatch conditions 40

Unsupervised adaptation of a denoising autoencoder by Bayesi...

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40th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2015

作者： Heymann, Jahn Haeb-Umbach, Reinhold Golik, Pavel Schluter, Ralf University of Paderborn Department of Communications Engineering Paderborn Germany RWTH Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen Aachen Germany

ISBN: (纸本)9781467369978

The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising Autoencoder (DA) both bring performance gains in severe single-channel speech recognition conditions. The first can be adjusted to different conditions by an appropriate parameter setting, while the latter needs to be trained on conditions similar to the ones expected at decoding time, making it vulnerable to a mismatch between training and test conditions. We use a DNN backend and study reverberant ASR under three types of mismatch conditions: different room reverberation times, different speaker to microphone distances and the difference between artificially reverberated data and the recordings in a reverberant environment. We show that for these mismatch conditions BFE can provide the targets for a DA. This unsupervised adaptation provides a performance gain over the direct use of BFE and even enables to compensate for the mismatch of real and simulated reverberant data. © 2015 IEEE.

关键词： deep neuronal networks denoising autoencoder feature enhancement robust speech recognition

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