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Modeling Punctuation Prediction as Machine Translation 8

Modeling Punctuation Prediction as Machine Translation

引用

8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Peitz, Stephan Freitag, Markus Mauser, Arne Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

Punctuation prediction is an important task in Spoken language Translation. The output of speech recognition systems does not typically contain punctuation marks. In this paper we analyze different methods for punctuation prediction and show improvements in the quality of the final translation output. In our experiments we compare the different approaches and show improvements of up to 0.8 BLEU points on the IWSLT 2011 English French Speech Translation of Talks task using a translation system to translate from unpunctuated to punctuated text instead of a language model based punctuation prediction method. Furthermore, we do a system combination of the hypotheses of all our different approaches and get an additional improvement of 0.4 points in BLEU. © IWSLT 2011. All rights reserved.

关键词： Forecasting

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Combining Translation and language Model Scoring for Domain-Specific Data Filtering 8

Combining Translation and Language Model Scoring for Domain-...

引用

8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Mansour, Saab Wuebker, Joern Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

The increasing popularity of statistical machine translation (SMT) systems is introducing new domains of translation that need to be tackled. As many resources are already available, domain adaptation methods can be applied to utilize these recourses in the most beneficial way for the new domain. We explore adaptation via filtering, using the cross-entropy scores to discard irrelevant sentences. We focus on filtering for two important components of an SMT system, namely the language model (LM) and the translation model (TM). Previous work has already applied LM cross-entropy based scoring for filtering. We argue that LM cross-entropy might be appropriate for LM filtering, but not as much for TM filtering. We develop a novel filtering approach based on a combined TM and LM cross-entropy scores. We experiment with two large-scale translation tasks, the Arabic-to-English and English-to-French IWSLT 2011 TED Talks MT tasks. For LM filtering, we achieve strong perplexity improvements which carry over to the translation quality with improvements up to +0.4% BLEU. For TM filtering, the combined method achieves small but consistent improvements over the standalone methods. As a side effect of adaptation via filtering, the fully fledged SMT system vocabulary size and phrase table size are reduced by a factor of at least 2 while up to +0.6% BLEU improvement is observed. © IWSLT 2011. All rights reserved.

关键词： computer aided language translation

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Using morpheme and syllable based sub-words for polish LVCSR

Using morpheme and syllable based sub-words for polish LVCSR

引用

36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011

作者： Shaik, M. Ali Basha El-Desoky Mousa, Amr Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition - Computer Science Department RWTH Aachen University 52056 Aachen Germany

ISBN: (纸本)9781457705397

Polish is a synthetic language with a high morpheme-per-word ratio. It makes use of a high degree of inflection leading to high out-of-vocabulary (OOV) rates, and high language Model (LM) perplexities. This poses a challenge for Large Vocabulary and Continuous Speech recognition (LVCSR) systems. Here, the use of morpheme and syllable based units is investigated for building sub-lexical LMs. A different type of sub-lexical units is proposed based on combining morphemic or syllabic units with corresponding pronunciations. Thereby, a set of grapheme-phoneme pairs called graphones are used for building LMs. A relative reduction of 3.5% in Word Error Rate (WER) is obtained with respect to a traditional system based on full-words. © 2011 IEEE.

关键词： Continuous speech recognition

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Hybrid language models using mixed types of sub-lexical units for open vocabulary German LVCSR

Hybrid language models using mixed types of sub-lexical unit...

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12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011

作者： Ali Basha Shaik, M. El-Desoky Mousa, Amr Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

German is a highly inflected language with a large number of words derived from the same root. It makes use of a high degree of word compounding leading to high Out-of-vocabulary (OOV) rates, and language Model (LM) perplexities. For such languages the use of sub-lexical units for Large Vocabulary Continuous Speech recognition (LVCSR) becomes a natural choice. In this paper, we investigate the use of mixed types of sub-lexical units in the same recognition lexicon. Namely, morphemic or syllabic units combined with pronunciations called graphones, normal graphemic morphemes or syllables along with full-words. This mixture of units is used for building hybrid LMs suitable for open vocabulary LVCSR where the system operates over an open, constantly changing vocabulary like in broadcast news, political debates, etc. A relative reduction of around 5.0% in Word Error Rate (WER) is obtained compared to a traditional full-words system. Moreover, around 40% of the OOVs are recognized. Copyright © 2011 ISCA.

关键词： Computational linguistics

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Morpheme based Factored language Models for German LVCSR

Morpheme based Factored Language Models for German LVCSR

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12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011

作者： El-Desoky Mousa, Amr Ali Basha Shaik, M. Schlüter, Ralf Ney, Hermann Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany

German is a highly inflectional language, where a large number of words can be generated from the same root. It makes a liberal use of compounding leading to high Out-of-vocabulary (OOV) rates, and poor language Model (LM) probability estimates. Therefore, the use of morphemes for language modeling is considered a better choice for Large Vocabulary Continuous Speech recognition (LVCSR) than the full-words. Thereby, better lexical coverage and less LM perplexities are achieved. On the other side, the use of Factored language Models (FLMs) is considered a successful approach that allows the integration of many information sources to get better LM probability estimates. In this paper, we try a combined methodology for language modeling where both morphological decomposition and factored language modeling are used in one model called morpheme based FLM. Finally, we obtain around 2.5% relative reduction in Word Error Rate (WER) with respect to a traditional full-words system. Copyright © 2011 ISCA.

关键词： Modeling languages

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The RWTH Aachen Machine Translation System for IWSLT 2011 8

The RWTH Aachen Machine Translation System for IWSLT 2011

引用

8th International Workshop on Spoken language Translation, IWSLT 2011

作者： Wuebker, Joern Huck, Matthias Mansour, Saab Freitag, Markus Feng, Minwei Peitz, Stephan Schmidt, Christoph Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Aachen Germany

In this paper the statistical machine translation (SMT) systems of RWTH Aachen University developed for the evaluation campaign of the International Workshop on Spoken language Translation (IWSLT) 2011 is presented. We participated in the MT (English-French, Arabic-English, Chinese-English) and SLT (English-French) tracks. Both hierarchical and phrase-based SMT decoders are applied. A number of different techniques are evaluated, including domain adaptation via monolingual and bilingual data selection, phrase training, different lexical smoothing methods, additional reordering models for the hierarchical system, various Arabic and Chinese segmentation methods, punctuation prediction for speech recognition output, and system combination. By application of these methods we can show considerable improvements over the respective baseline systems. © IWSLT 2011. All rights reserved.

关键词： Hierarchical systems

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Warp that smile on your face: Optimal and smooth deformations for face recognition

Warp that smile on your face: Optimal and smooth deformation...

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作者： Gass, Tobias Pishchulin, Leonid Dreuw, Philippe Ney, Hermann Computer Vision Laboratory ETH Zurich Switzerland Computer Vision and Multimodal Computing MPI Informatics Saarbruecken Germany Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany

ISBN: (纸本)9781424491407

In this work, we present novel warping algorithms for full 2D pixel-grid deformations for face recognition. Due to high variation in face appearance, face recognition is considered a very difficult task, especially if only a single reference image, for example a mug-shot, per face is available. Usually model-based approaches with additional training data are used to cope with several types of variation occurring in facial imaging. Image warping contrarily yields a distance measure which is invariant with regard to several types of variation. This allows for precise recognition even using only very few reference observations. Due to the computationally complex problem of optimal 2D warping, pseudo-2D warping-based approaches in the past represented strong approximations of the original problem, and were mainly successful on data with low variability or rectified images. We propose a novel 2D warping method which is globally optimal and makes no prior assumtions on the data variability besides two-dimensional smootheness constraints which both avoid local mirroring and gaps and significantly speed up the optimization. Furthermore, we show that occlusion handling is imperative to obtain smooth warpings in a variety of domains. We evaluate our novel algorithm on various well known databases, such as the AR-Face and CMU-PIE database, and provide a detailed comparison to existing warping approaches. We show that by using simple relative 2D constraints, strong local features and a kernel, which is robust w.r.t. occlusions, our computationally complex approaches outperform state-of-the-art results for recognizing faces under varying expressions, occlusions and poses. Most interestingly, we achieve higher accuracy using fewer training instances per class compared to methods learning a model of the 3D shape. © 2011 IEEE.

关键词： Face recognition

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THE RWTH 2010 QUAERO ASR EVALUATION SYSTEM FOR ENGLISH, FRENCH, AND GERMAN

THE RWTH 2010 QUAERO ASR EVALUATION SYSTEM FOR ENGLISH, FREN...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： M. Sundermeyer M. Nussbaum-Thom S. Wiesler C. Plahl A. El-Desoky Mousa S. Hahn D. Nolden R. Schluter H. Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University

ISBN: (纸本)9781457705380

Recognizing Broadcast Conversational (BC) speech data is a difficult task, which can be regarded as one of the major challenges beyond the recognition of Broadcast News (BN). This paper presents the automatic speech recognition systems developed by RWTH for the English, French, and German language which attained the best word error rates for English and German, and competitive results for the French task in the 2010 Quaero evaluation for BC and BN data. At the same time, the RWTH German system used the least amount of training data among all participants. Large reductions in word error rate were obtained by the incorporation of the new Bottleneck Multilayer Perceptron (MLP) features for all three languages. Additional improvements were obtained for the German system by applying a new language modeling technique, decomposing words into sublexical components.

关键词： automatic speech recognition multilayer perceptrons

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Powerful extensions to CRFS for grapheme to phoneme conversion

Powerful extensions to CRFS for grapheme to phoneme conversi...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Stefan Hahn Patrick Lehnen Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781457705380

Conditional Random Fields (CRFs) have proven to per form well on natural language processing tasks like name transliteration, concept tagging or grapheme-to-phoneme (g2p) conversion. The aim of this paper is to propose some extension to the state-of-the-art CRF systems for these tasks. Since the number of features can grow rapidly, a method for features selection is very helpful to boost performance. A combination of L1 and L2 regularization (elastic net) has been adopted and implemented within the Rprop optimization algorithm. Usually, dependencies on the target side are limited to bigram dependencies since the computational complexity grows exponentially with the history length. We present a modified CRF decoding where a conventional language model on target side is integrated into the CRF search process. Thus, larger contexts can be taken into account. Besides these two main parts, the already published margin-extension to the CRF training criterion has been adopted.

关键词： Training Context Context modeling Optimization Natural language processing Software algorithms Interpolation

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A comparative analysis of dynamic network decoding

A comparative analysis of dynamic network decoding

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： David Rybach Ralf Schlüter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781457705380

The use of statically compiled search networks for ASR systems using huge vocabularies and complex language models often becomes challenging in terms of memory requirements. Dynamic network decoders introduce additional computations in favor of significantly lower memory consumption. In this paper we investigate the properties of two well-known search strategies for dynamic network decoding, namely history conditioned tree search and WFST-based search using dynamic transducer composition. We analyze the impact of the differences in search graph representation, search space structure, and language model look-ahead techniques. Experiments on an LVCSR task illustrate the influence of the compared properties.

关键词： Decoding Transducers Hidden Markov models History Speech recognition Context Vocabulary

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