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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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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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Improvement of Context Dependent Modeling for Arabic Handwriting recognition

Improvement of Context Dependent Modeling for Arabic Handwri...

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International Workshop on Frontiers in Handwriting recognition

作者： Mahdi Hamdani Patrick Doetsch Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

This paper proposes the improvement of context dependent modeling for Arabic handwriting recognition. Since the number of parameters in context dependent models is huge, CART trees are used for state tying. This work is based on a new set of questions for the CART tree construction based on a "lossy mapping" categorization of the Arabic shapes. The used system is a combination of Hidden Markov Models and Recurrent Neural Networks using the hybrid approach. A comparison between a Neural network trained using the baseline labels and another one based on the CART tree labels is done. The experimental results show that the use of the CART labels for the Neural Network training beneficial. The lossy mapping based CART tree performed better than the baseline system. An absolute improvement of 2.9% in terms of Word Error Rate is performed on the test set of the Open Hart database.

关键词： Hidden Markov models Context Handwriting recognition Context modeling Training Shape Speech recognition

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RASR/NN: THE RWTH NEURAL NETWORK TOOLKIT FOR SPEECH recognition

RASR/NN: THE RWTH NEURAL NETWORK TOOLKIT FOR SPEECH RECOGNIT...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Simon Wiesler Alexander Richard Pavel Golik Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781479928941

This paper describes the new release of RASR - the open source version of the well-proven speech recognition toolkit developed and used at RWTH Aachen University. The focus is put on the implementation of the NN module for training neural network acoustic models. We describe code design, configuration, and features of the NN module. The key feature is a high flexibility regarding the network topology, choice of activation functions, training criteria, and optimization algorithm, as well as a built-in support for efficient GPU computing. The evaluation of run-time performance and recognition accuracy is performed exemplary with a deep neural network as acoustic model in a hybrid NN/HMM system. The results show that RASR achieves a state-of-the-art performance on a real-world large vocabulary task, while offering a complete pipeline for building and applying large scale speech recognition systems.

关键词： Speech recognition Acoustic models Neural network runtime performance Optimization algorithms speech recognition system Buildings

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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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ICFHR2014 Competition on Handwritten Text recognition on Transcriptorium Datasets (HTRtS)

ICFHR2014 Competition on Handwritten Text Recognition on Tra...

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International Workshop on Frontiers in Handwriting recognition

作者： Joan Andreu Sánchez Verónica Romero Alejandro H. Toselli Enrique Vidal Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

A contest on Handwritten Text recognition organised in the context of the ICFHR 2014 conference is described. Two tracks with increased freedom on the use of training data were proposed and three research groups participated in these two tracks. The handwritten images for this contest were drawn from an English data set which is currently being considered in the Tran scriptorium project. The goal of this project is to develop innovative, efficient and cost-effective solutions for the transcription of historical handwritten document images, focusing on four languages: English, Spanish, German and Dutch. For the English language, the so-called "Bentham collection" is being considered in Tran scriptorium. It encompasses a large set of manuscripts written by the renowned English philosopher and reformer Jeremy Bentham (1748-1832). A small subset of this collection has been chosen for the present HTR competition. The selected subset has been written by several hands (Bentham himself and his secretaries) and entails significant variabilities and difficulties regarding the quality of text images and writing styles. Training and test data were provided in the form of carefully segmented line images, along with the corresponding transcripts. The three participants achieved very good results, with transcription word error rates ranging from 15.0% down to 8.6%.

关键词： Training Hidden Markov models Histograms Artificial neural networks Text recognition Training data Adaptive optics

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A comparison of sequence-trained deep neural networks and recurrent neural networks optical modeling for handwriting recognition

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8791卷 199-219页

作者： Bluche, Théodore Ney, Hermann Kermorvant, Christopher A2iA SA Paris France Spoken Language Processing Group LIMSI CNRS Orsay France Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany

Long Short-Term Memory Recurrent Neural Networks are the current state-of-the-art in handwriting recognition. In speech recognition, Deep Multi-Layer Perceptrons (DeepMLPs) have become the standard acoustic model for Hidden Markov Models (HMMs). Although handwriting and speech recognition systems tend to include similar components and techniques, DeepMLPs are not used as optical model in unconstrained large vocabulary handwriting recognition. In this paper, we compare Bidirectional LSTM-RNNs with DeepMLPs for this task. We carried out experiments on two public databases of multi-line handwritten documents: Rimes and IAM. We show that the proposed hybrid systems yield performance comparable to the state-of-the-art, regardless of the type of features (hand-crafted or pixel values) and the neural network optical model (DeepMLP or RNN). © Springer International Publishing Switzerland 2014.

关键词： Deep neural networks

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THE RWTH ENGLISH LECTURE recognition SYSTEM

THE RWTH ENGLISH LECTURE RECOGNITION SYSTEM

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Simon Wiesler Kazuki Irie Zoltan Tuske Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany Ecole Centrale Paris Paris France

ISBN: (纸本)9781479928941

In this paper, we describe the RWTH speech recognition system for English lectures developed within the Translectures project. A difficulty in the development of an English lectures recognition system, is the high ratio of non-native speakers. We address this problem by using very effective deep bottleneck features trained on multilingual data. The acoustic model is trained on large amounts of data from different domains and with different dialects. Large improvements are obtained from unsupervised acoustic adaptation. Another challenge is the frequent use of technical terms and the wide range of topics. In our recognition system, slides, which are attached to most lectures, are used for improving lexical coverage and language model adaptation.

关键词： Lectures (teaching method) recognition system modelling languages speech recognition system English English language Acoustic models

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Open-Lexicon language Modeling Combining Word and Character Levels

Open-Lexicon Language Modeling Combining Word and Character ...

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International Workshop on Frontiers in Handwriting recognition

作者： Michal Kozielski Martin Matysiak Patrick Doetsch Ralf Schlöter Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Aachen Germany Rheinisch-Westfalische Technische Hochschule Aachen Aachen Nordrhein-Westfalen DE

In this paper we investigate different n-gram language models that are defined over an open lexicon. We introduce a character-level language model and combine it with a standard word-level language model in a back off fashion. The character-level language model is redefined and renormalized to assign zero probability to words from a fixed vocabulary. Furthermore we present a way to interpolate language models created at the word and character levels. The computation of character-level probabilities incorporates the across-word context. We compare perplexities on all words from the test set and on in-lexicon and OOV words separately on corpora of English and Arabic text.

关键词： Context Vocabulary Computational modeling Training Interpolation Standards Speech recognition

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Length-incremental Phrase Training for SMT 8

Length-incremental Phrase Training for SMT

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8th Workshop on Statistical Machine Translation, WMT 2013

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

ISBN: (纸本)9781937284572

We present an iterative technique to generate phrase tables for SMT, which is based on force-aligning the training data with a modified translation decoder. Different from previous work, we completely avoid the use of a word alignment or phrase extraction heuristics, moving towards a more principled phrase generation and probability estimation. During training, we allow the decoder to generate new phrases on-the-fly and increment the maximum phrase length in each iteration. Experiments are carried out on the IWSLT 2011 Arabic-English task, where we are able to reach moderate improvements on a state-of-the-art baseline with our training method. The resulting phrase table shows only a small overlap with the heuristically extracted one, which demonstrates the restrictiveness of limiting phrase selection by a word alignment or heuristics. By interpolating the heuristic and the trained phrase table, we can improve over the baseline by 0.5% BLEU and 0.5% TER. © 2013 Association for Computational Linguistics

关键词： Decoding

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