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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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On the Robustness of Transformer-Based Models to Different Linguistic Perturbations: A Case of Study in Irony Detection

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Expert Systems 2025年第6期42卷

作者： Ortega-Bueno, Reynier Fersini, Elisabetta Rosso, Paolo Pattern Recognition and Human Language Technology Center Universitat Politècnica de València Cami de Vera S/N Valencia Spain Department of Informatics Systems and Communication Università degli Studi di Milano-Bicocca Viale Sarca 336 Milan Italy ValgrAI–Valencian Graduate School and Research Network of Artificial Intelligence Valencia Valencia Spain

This study investigates the robustness of Transformer models in irony detection addressing various textual perturbations, revealing potential biases in training data concerning ironic and non-ironic classes. The perturbations involve three distinct approaches, each progressively increasing in complexity. The first approach is word masking, which employs wild-card characters or utilises BERT-specific masking through the mask token provided by BERT models. The second approach is word substitution, replacing the bias word with a contextually appropriate alternative. Lastly, paraphrasing generates a new phrase while preserving the original semantic meaning. We leverage Large language Models (GPT 3.5 Turbo) and human inspection to ensure linguistic correctness and contextual coherence for word substitutions and paraphrasing. The results indicate that models are susceptible to these perturbations, and paraphrasing and word substitution demonstrate the most significant impact on model predictions. The irony class appears to be particularly challenging for models when subjected to these perturbations. The SHAP and LIME methods are used to correlate variations in attribution scores with prediction errors. A notable difference in the Total Variation of attribution scores is observed between original examples and cases involving bias word substitution or masking. Among the corpora used, TwSemEval2018 emerges as the most challenging. Regarding model performance, Transformer-based models such as RoBERTa and BERTweet demonstrate superior overall performance addressing these perturbations. This research contributes to understanding the robustness and limitations of irony detection models, highlighting areas for improvement in model design and training data curation. © 2025 John Wiley & Sons Ltd.

关键词： Semantics

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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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A neural, interactive-predictive system for multimodal sequence to sequence tasks 57

A neural, interactive-predictive system for multimodal seque...

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57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, ACL 2019

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politcnica de Valncia Valncia Spain

ISBN: (纸本)9781950737499

We present a demonstration of a neural interactive-predictive system for tackling mul- timodal sequence to sequence tasks. The sys- tem generates text predictions to different se- quence to sequence tasks: machine translation, image and video captioning. These predictions are revised by a human agent, who introduces corrections in the form of characters. The sys- tem reacts to each correction, providing alter- native hypotheses, compelling with the feed- back provided by the user. The final objective is to reduce the human effort required during this correction process. This system is implemented following a client-server architecture. For accessing the system, we developed a website, which com- municates with the neural model, hosted in a local server. From this website, the dif- ferent tasks can be tackled following the interactive-predictive framework. We open- source all the code developed for building this system. The demonstration in hosted in http://***/ interactive-seq2seq. © 2019 All rights reserved.

关键词： Websites

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Handwritten text recognition results on the Bentham collection with improved classical N-Gram-HMM methods 15

Handwritten text recognition results on the Bentham collecti...

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3rd International Workshop on Historical Document Imaging and Processing, HIP 2015

作者： Toselli, Alejandro H. Vidal, Enrique Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Spain

ISBN: (纸本)9781450336024

Handwritten Text recognition experiments and results are presented on the historical Bentham text image dataset used in the ICFHR-2014 HTRtS competition. The successful segmentation-free holistic framework is adopted, using traditional modelling approaches based on Hidden Markov optical character models (HMM) and an N-gram language model (LM). Departing from the very basic N-gram-HMM baseline system provided in HTRtS, several improvements are made in text image feature extraction and LM and HMM modeling, including more accurate HMM training by means of discriminative training. As a result, we achieve similar recognition accuracy as some of the best performing (single, uncombined) systems based on (recurrent) Neural Networks (NN), using identical training and testing data. The traditional N-gram/HMM framework offers several advantages over modern approaches based on (hybrid, recurrent) NNs. Perhaps the most important is the much faster training of HMMs and the well understood stability of the results of Baum-Welch training. These advantages become very important when dealing with many historical document collections, which are typically huge and entail very high degrees of variability, making it generally diffcult to re-use models trained on previous collections. © 2015 ACM.

关键词： Recurrent neural networks

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Interactive-Predictive Neural Multimodal Systems 9th

Interactive-Predictive Neural Multimodal Systems

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9th Iberian Conference on pattern recognition and Image Analysis, IbPRIA 2019

作者： Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València Valencia Spain

ISBN: (纸本)9783030313319

Despite the advances achieved by neural models in sequence to sequence learning, exploited in a variety of tasks, they still make errors. In many use cases, these are corrected by a human expert in a posterior revision process. The interactive-predictive framework aims to minimize the human effort spent on this process by considering partial corrections for iteratively refining the hypothesis. In this work, we generalize the interactive-predictive approach, typically applied in to machine translation field, to tackle other multimodal problems namely, image and video captioning. We study the application of this framework to multimodal neural sequence to sequence models. We show that, following this framework, we approximately halve the effort spent for correcting the outputs generated by the automatic systems. Moreover, we deploy our systems in a publicly accessible demonstration, that allows to better understand the behavior of the interactive-predictive framework. © 2019, Springer Nature Switzerland AG.

关键词： pattern recognition

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Interactive-predictive translation based on multiple word-segments 19

Interactive-predictive translation based on multiple word-se...

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19th Annual Conference of the European Association for Machine Translation, EAMT 2016

作者： Domingo, Miguel Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Camino de Vera s/n Valencia46022 Spain

Current machine translation systems require human revision to produce high-quality translations. This is achieved through a post-editing process or by means of an interactive human-computer collaboration. Most protocols belonging to the last scenario follow a left-to-right strategy, where the prefix of the translation is iteratively increased by successive validations and corrections made by the user. In this work, we propose a new interactive protocol which allows the user to validate all correct word sequences in the translation generated by the system, breaking the left-to-right barrier. We evaluated our proposal through simulated experiments, obtaining large reductions of the human effort. © 2016, European Association for Machine Translation. All rights reserved.

关键词： Machine translation

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Adapting neural machine translation with parallel synthetic data 2

Adapting neural machine translation with parallel synthetic ...

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2nd Conference on Machine Translation, WMT 2017

作者： Chinea-Ríos, Mara Peris, Álvaro Casacuberta, Francisco Pattern Recognition and Human Language Technology Research Center Universitat Politècnica de València València Spain

ISBN: (纸本)9781945626968

Recent works have shown that the usage of a synthetic parallel corpus can be effectively exploited by a neural machine translation system. In this paper, we propose a new method for adapting a general neural machine translation system to a specific task, by exploiting synthetic data. The method consists in selecting, from a large monolingual pool of sentences in the source language, those instances that are more related to a given test set. Next, this selection is automatically translated and the general neural machine translation system is fine-tuned with these data. For evaluating the adaptation method, we first conducted experiments in two controlled domains, with common and well-studied corpora. Then, we evaluated our proposal on a real e-commerce task, yielding consistent improvements in terms of translation quality. © 2017 Association for Computational Linguistics

关键词： Neural machine translation

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