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Exploring E2E speech recognition systems for new languages 4

Exploring E2E speech recognition systems for new languages

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

作者： Bernath, Conrad Álvarez, Aitor Arzelus, Haritz Martínez-Hinarejos, Carlos-D. Human Speech and Language Technology Group Vicomtech Spain Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Spain

Over the last few years, advances in both machine learning algorithms and computer hardware have led to significant improvements in speech recognition technology, mainly through the use of Deep Learning paradigms. As it was amply demonstrated in different studies, Deep Neural Networks (DNNs) have already outperformed traditional Gaussian Mixture Models (GMMs) at acoustic modeling in combination with Hidden Markov Models (HMMs). More recently, new attempts have focused on building end-to-end (E2E) speech recognition architectures, especially in languages with many resources like English and Chinese, with the aim of overcoming the performance of LSTM-HMM and more conventional systems. The aim of this work is first to present the different techniques that have been applied to enhance state-of-the-art E2E systems for American English using publicly available datasets. Secondly, we describe the construction of E2E systems for Spanish and Basque, and explain the strategies applied to overcome the problem of the limited availability of training data, especially for Basque as a low-resource language. At the evaluation phase, the three E2E systems are also compared with LSTM-HMM based recognition engines built and tested with the same datasets. © 4th International Conference, IberSPEECH 2018.

关键词： Hidden Markov models

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Limitations and Challenges of Unsupervised Cross-lingual Pre-training 15

Limitations and Challenges of Unsupervised Cross-lingual Pre...

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15th Conference of the Association for Machine Translation in the Americas, AMTA 2022

作者： Zaragoza, Martín Quesada Casacuberta, Francisco Research Center of Pattern Recognition and Human Language Technology Universitat Politècnica de València Valencia46022 Spain

Cross-lingual alignment methods for monolingual language representations have received notable attention in recent years. However, their use in machine translation pre-training remains scarce. This work tries to shed light on the effects of some of the factors that play a role in cross-lingual pre-training, both for cross-lingual mappings and their integration in supervised neural models. The results show that unsupervised cross-lingual methods are effective at inducing alignment even for distant languages and they benefit noticeably from subword information. However, we find that their effectiveness as pre-training models in machine translation is severely limited due to their cross-lingual signal being easily distorted by the principal network during training. Moreover, the learned bilingual projection is too restrictive to allow said network to learn properly when the embedding weights are frozen. © AMTA 2022 - 15th Conference of the Association for Machine Translation in the Americas, Proceedings.

关键词： Machine translation

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A multimodal crowdsourcing framework for transcribing historical handwritten documents 16

A multimodal crowdsourcing framework for transcribing histor...

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16th ACM Symposium on Document Engineering, DocEng 2016

作者： Granell, Emilio Martínez-Hinarejos, Carlos D. Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camino Vera s/n 46022 Valencia Spain

ISBN: (纸本)9781450344388

Transcription of handwritten historical documents is one of the main topics in document analysis systems, due to cultural reasons. State-of-the-art handwritten text recognition systems allow to speed up the transcription task. Currently, this automatic transcription is far from perfect, and human expert revision is required in order to obtain the actual transcription. In this context, crowdsourcing emerged as a powerful tool for massive transcription at a relatively low cost, since the supervision effort of professional transcribers may be dramatically reduced. However, current transcription crowdsourcing platforms are mainly limited to the use of nonmobile devices, since the use of keyboards in mobile devices is not friendly enough for most users. This work presents the alternative of using speech dictation of handwritten text lines as transcription source in a crowdsourcing platform. The experiments explore how an initial handwritten text recognition hypothesis can be improved by using the contribution of speech recognition from several speakers, providing as a final result a better hypothesis to be amended by a professional transcriber with less effort.

关键词： Speech recognition

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Creating the best development corpus for Statistical Machine Translation systems 21

Creating the best development corpus for Statistical Machine...

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21st Annual Conference of the European Association for Machine Translation, EAMT 2018

作者： Chinea-Rios, Mara Sanchis-Trilles, Germán Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain Sciling València Spain

ISBN: (纸本)9788409019014

We propose and study three different novel approaches for tackling the problem of development set selection in Statistical Machine Translation. We focus on a scenario where a machine translation system is leveraged for translating a specific test set, without further data from the domain at hand. Such test set stems from a real application of machine translation, where the texts of a specific e-commerce were to be translated. For developing our development-set selection techniques, we first conducted experiments in a controlled scenario, where labelled data from different domains was available, and evaluated the techniques both with classification and translation quality metrics. Then, the best-performing techniques were evaluated on the e-commerce data at hand, yielding consistent improvements across two language directions. © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.

关键词： Machine translation

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Advances on the Transcription of Historical Manuscripts based on Multimodality, Interactivity and Crowdsourcing 4

Advances on the Transcription of Historical Manuscripts base...

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

作者： Granell, Emilio Martínez-Hinarejos, Carlos-D. Romero, Verónica Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Camí de Vera s/n València46022 Spain

The transcription of digitalised documents is useful to ease the digital access to their contents. Natural language technologies, such as Automatic Speech recognition (ASR) for speech audio signals and Handwritten Text recognition (HTR) for text images, have become common tools for assisting transcribers, by providing a draft transcription from the digital document that they may amend. This draft is useful when it presents an error rate low enough to make the amending process more comfortable than a complete transcription from scratch. The work described in this thesis is focused on the improvement of the transcription offered by an HTR system from three scenarios: multimodality, interactivity and crowdsourcing. The image transcription can be obtained by dictating their textual contents to an ASR system. Besides, when both sources of information (image and speech) are available, a multimodal combination is possible, and this can be used to provide assistive systems with additional sources of information. Moreover, speech dictation can be used in a multimodal crowdsourcing platform, where collaborators may provide their speech by using mobile devices. Different solutions for each scenario were tested on two Spanish historical manuscripts, obtaining statistically significant improvements. © 4th International Conference, IberSPEECH 2018.

关键词： Crowdsourcing

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Improving Transcription of Manuscripts with Multimodality and Interaction 4

Improving Transcription of Manuscripts with Multimodality an...

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

State-of-the-art Natural language recognition systems allow transcribers to speed-up the transcription of audio, video or image documents. These systems provide transcribers an initial draft transcription that can be corrected with less effort than transcribing the documents from scratch. However, even the drafts offered by the most advanced systems based on Deep Learning contain errors. Therefore, the supervision of those drafts by a human transcriber is still necessary to obtain the correct transcription. This supervision can be eased by using interactive and assistive transcription systems, where the transcriber and the automatic system cooperate in the amending process. Moreover, the interactive system can combine different sources of information in order to improve their performance, such as text line images and the dictation of their textual contents. In this paper, the performance of a multimodal interactive and assistive transcription system is evaluated on one Spanish historical manuscript. Although the quality of the draft transcriptions provided by a Handwriting Text recognition system based on Deep Learning is pretty good, the proposed interactive and assistive approach reveals an additional reduction of transcription effort. Besides, this effort reduction is increased when using speech dictations over an Automatic Speech recognition system, allowing for a faster transcription process. © 4th International Conference, IberSPEECH 2018.

关键词： human computer interaction

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Bag-of-visual-words models for adult image classification and filtering

Bag-of-visual-words models for adult image classification an...

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作者： Deselaers, Thomas Pimenidis, Lexi Ney, Hermann Computer Science Department Human Language Technology and Pattern Recognition RWTH Aachen University Aachen Germany Security and Privacy Research RWTH Aachen University Aachen Germany

ISBN: (纸本)9781424421756

We present a method to classify images into different categories of pornographic content to create a system for filtering pornographic images from network traffic. Although different systems for this application were presented in the past, most of these systems are based on simple skin colour features and have rather poor performance. Recent advances in the image recognition field in particular for the classification of objects have shown that bag-of-visual-words-approaches are a good method for many image classification problems. The system we present here, is based on this approach, uses a task-specific visual vocabulary and is trained and evaluated on an image database of 8500 images from different categories. It is shown that it clearly outperforms earlier systems on this dataset and further evaluation on two novel web-traffic collections shows the good performance of the proposed system. © 2008 IEEE.

关键词： Image classification

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Comparison of Conventional Hybrid and CTC/Attention Decoders for Continuous Visual Speech recognition 30

Comparison of Conventional Hybrid and CTC/Attention Decoders...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on language Resources and Evaluation, LREC-COLING 2024

作者： Gimeno-Gómez, David Martínez-Hinarejos, Carlos D. Pattern Recognition and Human Language Technologies Research Center Universitat Politècnica de València Camino de Vera s/n València46022 Spain

ISBN: (纸本)9782493814104

Thanks to the rise of deep learning and the availability of large-scale audio-visual databases, recent advances have been achieved in Visual Speech recognition (VSR). Similar to other speech processing tasks, these end-to-end VSR systems are usually based on encoder-decoder architectures. While encoders are somewhat general, multiple decoding approaches have been explored, such as the conventional hybrid model based on Deep Neural Networks combined with Hidden Markov Models (DNN-HMM) or the Connectionist Temporal Classification (CTC) paradigm. However, there are languages and tasks in which data is scarce, and in this situation, there is not a clear comparison between different types of decoders. Therefore, we focused our study on how the conventional DNN-HMM decoder and its state-of-the-art CTC/Attention counterpart behave depending on the amount of data used for their estimation. We also analyzed to what extent our visual speech features were able to adapt to scenarios for which they were not explicitly trained, either considering a similar dataset or another collected for a different language. Results showed that the conventional paradigm reached recognition rates that improve the CTC/Attention model in data-scarcity scenarios along with a reduced training time and fewer parameters. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： Hidden Markov models

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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...

引用

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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Morpheme-based feature-rich language models using Deep Neural Networks for LVCSR of Egyptian Arabic

Morpheme-based feature-rich language models using Deep Neura...

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2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013

作者： El-Desoky Mousa, Amr Kuo, Hong-Kwang Jeff Mangu, Lidia Soltau, Hagen Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University 52056 Aachen Germany IBM T. J. Watson Research Center Yorktown Heights NY 10598 United States

ISBN: (纸本)9781479903566

Egyptian Arabic (EA) is a colloquial version of Arabic. It is a low-resource morphologically rich language that causes problems in Large Vocabulary Continuous Speech recognition (LVCSR). Building LMs on morpheme level is considered a better choice to achieve higher lexical coverage and better LM probabilities. Another approach is to utilize information from additional features such as morphological tags. On the other hand, LMs based on Neural Networks (NNs) with a single hidden layer have shown superiority over the conventional n-gram LMs. Recently, Deep Neural Networks (DNNs) with multiple hidden layers have achieved better performance in various tasks. In this paper, we explore the use of feature-rich DNN-LMs, where the inputs to the network are a mixture of words and morphemes along with their features. Significant Word Error Rate (WER) reductions are achieved compared to the traditional word-based LMs. © 2013 IEEE.

关键词： Deep neural networks

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