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arXiv 2025年

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 València Comunitat Valenciana46022 Spain

Visual speech recognition remains an open research problem where different challenges must be considered by dispensing with the auditory sense, such as visual ambiguities, the inter-personal variability among speakers, and the complex modeling of silence. Nonetheless, recent remarkable results have been achieved in the field thanks to the availability of large-scale databases and the use of powerful attention mechanisms. Besides, multiple languages apart from English are nowadays a focus of interest. This paper presents noticeable advances in automatic continuous lipreading for Spanish. First, an end-to-end system based on the hybrid CTC/Attention architecture is presented. Experiments are conducted on two corpora of disparate nature, reaching state-of-the-art results that significantly improve the best performance obtained to date for both databases. In addition, a thorough ablation study is carried out, where it is studied how the different components that form the architecture influence the quality of speech recognition. Then, a rigorous error analysis is carried out to investigate the different factors that could affect the learning of the automatic system. Finally, a new Spanish lipreading benchmark is consolidated. Code and trained models are available at https://***/david-gimeno/evaluating-end2end-spanish-lipreading. © 2025, CC BY-NC-ND.

关键词： Continuous speech recognition

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Ground-truth generation through crowdsourcing with probabilistic indexes

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Neural Computing and Applications 2024年第30期36卷 18879-18895页

作者： Sánchez, Joan Andreu Vidal, Enrique Bosch, Vicente Quirós, Lorenzo Pattern Recognition and Human Language Technologies Center Universitat Politècnica de València València46022 Spain tranSkriptorium AI València Spain

Automatic transcription of large series of historical handwritten documents generally aims at allowing to search for textual information in these documents. However, automatic transcripts often lack the level of accuracy needed for reliable text indexing and search purposes. Probabilistic Indexing (PrIx) offers a unique alternative to raw transcripts. Since it needs training data to achieve good search performance, PrIx-based crowdsourcing techniques are introduced in this paper to gather the required data. In the proposed approach, PrIx confidence measures are used to drive a correction process in which users can amend errors and possibly add missing text. In a further step, corrected data are used to retrain the PrIx models. Results on five large series are reported which show consistent improvements after retraining. However, it can be argued whether the overall costs of the crowdsourcing operation pay off for the improvements, or perhaps it would have been more cost-effective to just start with a larger and cleaner amount of professionally produced training transcripts. © The Author(s) 2024.

关键词： Crowdsourcing

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The Vicomtech-PRHLT Speech Transcription Systems for the IberSPEECH-RTVE 2018 Speech to Text Transcription Challenge 4

The Vicomtech-PRHLT Speech Transcription Systems for the Ibe...

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

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

This paper describes our joint submission to the IberSPEECH-RTVE Speech to Text Transcription Challenge 2018, which calls for automatic speech transcription systems to be evaluated in realistic TV shows. With the aim of building and evaluating systems, RTVE licensed around 569 hours of different TV programs, which were processed, re-aligned and revised in order to discard segments with imperfect transcriptions. This task reduced the corpus to 136 hours that we considered as nearly perfectly aligned audios and that we employed as in-domain data to train acoustic models. A total of 6 systems were built and presented to the evaluation challenge, three systems per condition. These recognition engines are different versions, evolution and configurations of two main architectures. The first architecture includes an hybrid LSTM-HMM acoustic model, where bidirectional LSTMs were trained to provide posterior probabilities for the HMM states. The language model corresponds to modified Kneser- Ney smoothed 3-gram and 9-gram models used for decoding and re-scoring of the lattices respectively. The second architecture includes an End-To-End based recognition system, which combines 2D convolutional neural networks as spectral feature extractor from spectrograms with bidirectional Gated Recurrent Units as RNN acoustic models. A modified Kneser-Ney smoothed 5-gram model was also integrated to re-score the E2E hypothesis. All the systems' outputs were then punctuated using bidirectional RNN models with attention mechanism and capitalized through recasing techniques. © 4th International Conference, IberSPEECH 2018.

关键词： Speech recognition

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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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Online optimisation of log-linear weights in interactive machine translation 9

Online optimisation of log-linear weights in interactive mac...

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9th International Conference on language Resources and Evaluation, LREC 2014

作者： Rios, Mara Chinea Sanchis-Trilles, Germán Ortiz-Martínez, Daniel Casacuberta, Francisco Pattern Recognition and Human Language Technologies Group Universitat Politècnica de València Camino de Vera s/n Valencia Spain

ISBN: (纸本)9782951740884

Whenever the quality provided by a machine translation system is not enough, a human expert is required to correct the sentences provided by the machine translation system. In such a setup, it is crucial that the system is able to learn from the errors that have already been corrected. In this paper, we analyse the applicability of discriminative ridge regression for learning the log-linear weights of a state-of-the - art machine translation system underlying an interactive machine translation framework, with encouraging results.

关键词： Machine translation

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Online learning of log-linear weights in interactive machine translation

Online learning of log-linear weights in interactive machine...

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Joint 7th Spanish Speech Technology Workshop and the Iberian SLTech Workshop, IberSPEECH 2012

作者： López-Salcedo, Francisco-Javier Sanchis-Trilles, Germán Casacuberta, Francisco Pattern Recognition and Human Language Technologies Group Departamento de Sistemas Informáticos Y Computación Universitat Politècnica de València Spain

ISBN: (纸本)9783642352911

Whenever the quality provided by a machine translation system is not enough, a human expert is required to correct the sentences provided by the machine translation system. In this environment, the human translator is generating bilingual data after each translation has been marked as correct, and expects the system to be able to learn from the errors made. In this paper, we analyse the appropriateness of discriminative ridge regression for adapting the scaling factors of a state-of-the-art machine translation system within a conventional post-editing scenario and also within an interactive machine translation setup. Results show that the strategies applied in the former setup cannot be directly applied in the latter framework. Hence, the discriminative ridge regression is revised and adapted for the interactive machine translation framework, with encouraging results. © 2012 Springer-Verlag.

关键词： Machine translation

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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

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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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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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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Offline Features for Classifying Handwritten Math Symbols with Recurrent Neural Networks

Offline Features for Classifying Handwritten Math Symbols wi...

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International Conference on pattern recognition

作者： Francisco Álvaro Joan-Andreu Sánchez José-Miguel Benedí Pattern Recognition and Human Language Technologies Universitat Politècnica de València Spain

In mathematical expression recognition, symbol classification is a crucial step. Numerous approaches for recognizing handwritten math symbols have been published, but most of them are either an online approach or a hybrid approach. There is an absence of a study focused on offline features for handwritten math symbol recognition. Furthermore, many papers provide results difficult to compare. In this paper we assess the performance of several well-known offline features for this task. We also test a novel set of features based on polar histograms and the vertical repositioning method for feature extraction. Finally, we report and analyze the results of several experiments using recurrent neural networks on a large public database of online handwritten math expressions. The combination of online and offline features significantly improved the recognition rate.

关键词： Feature extraction Text recognition Vectors Handwriting recognition Hidden Markov models Databases Training

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