Publications

@phdthesis{Garcés2024,
title = {Automatic speech recognition and machine translation with deep neural networks for open educational resources, parliamentary contents and broadcast media},
author = {Garcés Díaz-Munío, Gonçal},
url = {http://hdl.handle.net/10251/212454
https://www.upv.es/pls/oalu/sic_ted.mostrar_tesis?p_num_reg=12900
https://github.com/gonsalet/ASR_and_MT_for_educational_parliamentary_and_broadcast_media},
doi = {10.4995/Thesis/10251/212454},
year = {2024},
date = {2024-11-25},
school = {Universitat Politècnica de València},
abstract = {In the last decade, automatic speech recognition (ASR) and machine translation (MT) have improved enormously through the use of constantly evolving deep neural network (DNN) models. If at the beginning of the 2010s the then pre-DNN ASR and MT systems were ready to tackle with success some real-life applications such as offline video lecture transcription and translation, now in the 2020s much more challenging applications are within grasp, such as live broadcast media subtitling. At the same time in this period, media accessibility for everyone, including deaf and hard-of-hearing people, is being given more and more importance. ASR and MT, in their current state, are powerful tools to increase the coverage of accessibility measures such as subtitles, transcriptions and translations, also as a way of providing multilingual access to all types of content. In this PhD thesis, we present research results on automatic speech recognition and machine translation based on deep neural networks in three very active domains: open educational resources, parliamentary contents and broadcast media. Regarding open educational resources (OER), we first present work on the evaluation and post-editing of ASR and MT with intelligent interaction approaches, as carried out in the framework of EU project transLectures: Transcription and Translation of Video Lectures. The results obtained confirm that the intelligent interaction approach can make post-editing automatic transcriptions and translations even more cost-effective. Then, in the context of subsequent EU project X5gon, we present research on developing DNN-based neural MT systems, and making the most of larger MT corpora through automatic data filtering. This work resulted in a first-rank classification in an international evaluation campaign on MT, and we show how these new NMT systems improved the quality of multilingual subtitles in real OER scenarios. In the also growing domain of language technologies for parliamentary contents, we describe research on speech data curation techniques for streaming ASR in the context of European Parliament debates. This research resulted in the release of Europarl-ASR, a new, large speech corpus for streaming ASR system training and evaluation, as well as for the benchmarking of speech data curation techniques. Finally, we present work in a domain on the edge of the state of the art for ASR and MT: the live subtitling of broadcast media, in the context of the 2020–2023 R&D collaboration agreement between the Valencian public broadcaster À Punt and the Universitat Politècnica de València for real-time computer assisted subtitling of media contents. This research has resulted in the deployment of high-quality, low-latency, real-time streaming ASR systems for a less-spoken language (Catalan) and a widely spoken language (Spanish) in a real broadcast use case.},
note = {advisers: Alfons Juan Ciscar and Jorge Civera Saiz},
keywords = {Automatic Speech Recognition, Broadcast Media, Deep Neural Networks, Machine Translation, Open Educational Resources, Parliamentary Contents},
pubstate = {published},
tppubtype = {phdthesis}
}

@inproceedings{deMartos2022,
title = {Doblaje automático de vídeo-charlas educativas en UPV[Media]},
author = {Pérez González de Martos, Alejandro AND Giménez Pastor, Adrià AND Jorge Cano, Javier AND Javier Iranzo-Sánchez AND Joan Albert Silvestre-Cerdà AND Garcés Díaz-Munío, Gonçal V. AND Pau Baquero-Arnal AND Sanchis Navarro, Alberto AND Civera Sáiz, Jorge AND Juan Ciscar, Alfons AND Turró Ribalta, Carlos},
doi = {10.4995/INRED2022.2022.15844},
year = {2022},
date = {2022-01-01},
booktitle = {Proc. of VIII Congrés d'Innovació Educativa i Docència en Xarxa (IN-RED 2022)},
pages = {557--570},
address = {València (Spain)},
abstract = {More and more universities are banking on the production of digital content to support online or blended learning in higher education. Over the last years, the MLLP research group has been working closely with the UPV's ASIC media services in order to enrich educational multimedia resources through the application of natural language processing technologies including automatic speech recognition, machine translation and text-to-speech. In this work, we present the steps that are being followed for the comprehensive translation of these materials, specifically through (semi-)automatic dubbing by making use of state-of-the-art speaker-adaptive text-to-speech technologies.},
keywords = {automatic dubbing, Automatic Speech Recognition, Machine Translation, OER, text-to-speech},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Iranzo-Sánchez2019,
title = {The MLLP-UPV Supervised Machine Translation Systems for WMT19 News Translation Task},
author = {Iranzo-Sánchez, Javier and Garcés Díaz-Munío, Gonçal V. and Civera, Jorge and Juan, Alfons},
url = {https://www.mllp.upv.es/wp-content/uploads/2019/09/poster-1.pdf},
doi = {10.18653/v1/W19-5320},
year = {2019},
date = {2019-01-01},
booktitle = {Proc. of Fourth Conference on Machine Translation (WMT19)},
pages = {218-224},
address = {Florence (Italy)},
abstract = {[EN] This paper describes the participation of the MLLP research group of the Universitat Politècnica de València in the WMT 2019 News Translation Shared Task. In this edition, we have submitted systems for the German ↔ English and German ↔ French language pairs, participating in both directions of each pair. Our submitted systems, based on the Transformer architecture, make ample use of data filtering, synthetic data and domain adaptation through fine-tuning.

[CA] "Els sistemes de traducció automàtica supervisada de l'MLLP-UPV per a la tasca de traducció de notícies de WMT19": En aquest article descrivim la participació del grup de recerca MLLP de la Universitat Politècnica de València en la competició de traducció de notícies de WMT 2019. En aquesta edició, hem presentat sistemes per a les combinacions de traducció alemany ↔ anglés i alemany ↔ francés (en ambdós sentits). Els sistemes presentats, basats en l'arquitectura Transformer, fan un ús extens del filtratge de dades, les dades sintètiques i l'ajust fi amb adaptació al domini.},
keywords = {Machine Translation, Neural Machine Translation, WMT19 News Translation},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Iranzo-Sánchez2018,
title = {The MLLP-UPV German-English Machine Translation System for WMT18},
author = {Iranzo-Sánchez, Javier and Baquero-Arnal, Pau and Garcés Díaz-Munío, Gonçal V. and Martínez-Villaronga, Adrià and Civera, Jorge and Juan, Alfons},
url = {http://dx.doi.org/10.18653/v1/W18-6414
https://www.mllp.upv.es/wp-content/uploads/2018/11/wmt18_mllp-upv_poster.pdf},
year = {2018},
date = {2018-01-01},
booktitle = {Proc. of the Third Conference on Machine Translation (WMT18), Volume 2: Shared Task Papers},
pages = {422--428},
address = {Brussels (Belgium)},
abstract = {[EN] This paper describes the statistical machine translation system built by the MLLP research group of Universitat Politècnica de València for the German>English news translation shared task of the EMNLP 2018 Third Conference on Machine Translation (WMT18). We used an ensemble of Transformer architecture–based neural machine translation systems. To train our system under "constrained" conditions, we filtered the provided parallel data with a scoring technique using character-based language models, and we added parallel data based on synthetic source sentences generated from the provided monolingual corpora.

[CA] "El sistema de traducció automàtica alemany>anglés de l'MLLP-UPV per a WMT18": En aquest article descrivim el sistema de traducció automàtica estadística creat pel grup d'investigació MLLP de la Universitat Politècnica de València per a la competició de traducció de notícies alemany>anglés de la Third Conference on Machine Translation (WMT18, associada a la conferència EMNLP 2018). Hem utilitzat una combinació de sistemes de traducció automàtica neuronal basats en l'arquitectura Transformer. Per a entrenar el nostre sistema en la categoria "fitada" (només amb els corpus lingüístics oficials de la competició), hem filtrat les dades paral·leles disponibles amb una tècnica que assigna puntuacions utilitzant models de llenguatge de caràcters, i hem afegit dades paral·leles basades en frases d'origen sintètiques generades a partir dels corpus monolingües disponibles.},
keywords = {Data Selection, Machine Translation, Neural Machine Translation, WMT18 news translation},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Silvestre-Cerdà2012b,
title = {transLectures},
author = {Silvestre-Cerdà, Joan Albert and Del Agua, Miguel and Gonçal Garcés and Guillem Gascó and Adrià Giménez-Pastor and Adrià Martínez and Pérez González de Martos, Alejandro and Isaías Sánchez and Serrano Martínez-Santos, Nicolás and Rachel Spencer and Valor Miró, Juan Daniel and Jesús Andrés-Ferrer and Jorge Civera and Alberto Sanchís and Alfons Juan},
url = {http://hdl.handle.net/10251/37290
http://lorien.die.upm.es/~lapiz/rtth/JORNADAS/VII/IberSPEECH2012_OnlineProceedings.pdf
https://web.archive.org/web/20130609073144/http://iberspeech2012.ii.uam.es/IberSPEECH2012_OnlineProceedings.pdf
http://www.mllp.upv.es/wp-content/uploads/2015/04/1209IberSpeech.pdf},
year = {2012},
date = {2012-11-22},
booktitle = {Proceedings (Online) of IberSPEECH 2012},
pages = {345--351},
address = {Madrid (Spain)},
abstract = {[EN] transLectures (Transcription and Translation of Video Lectures) is an EU STREP project in which advanced automatic speech recognition and machine translation techniques are being tested on large video lecture repositories. The project began in November 2011 and will run for three years. This paper will outline the project's main motivation and objectives, and give a brief description of the two main repositories being considered: VideoLectures.NET and poliMèdia. The first results obtained by the UPV group for the poliMedia repository will also be provided.

[CA] transLectures (Transcription and Translation of Video Lectures) és un projecte del 7PM de la Unió Europea en el qual s'estan posant a prova tècniques avançades de reconeixement automàtic de la parla i de traducció automàtica sobre grans repositoris digitals de vídeos docents. El projecte començà al novembre de 2011 i tindrà una duració de tres anys. En aquest article exposem la motivació i els objectius del projecte, i descrivim breument els dos repositoris principals sobre els quals es treballa: VideoLectures.NET i poliMèdia. També oferim els primers resultats obtinguts per l'equip de la UPV al repositori poliMèdia.},
keywords = {Accessibility, Automatic Speech Recognition, Education, Intelligent Interaction, Language Technologies, Machine Translation, Massive Adaptation, Multilingualism, Opencast Matterhorn, Video Lectures},
pubstate = {published},
tppubtype = {inproceedings}
}