Accessibility Automatic Speech Recognition Computer-assisted transcription Confidence measures Deep Neural Networks Docencia en Red Education language model adaptation Language Modeling Language Technologies Length modelling Log-linear models Machine Translation Massive Adaptation Models basats en seqüències de paraules Multilingualism Neural Machine Translation Opencast Matterhorn Polimedia Simultaneous Speech Translation Sliding window Speaker adaptation Speech Recognition Speech Translation Statistical machine translation streaming text-to-speech transcripciones video lecture repositories Video Lectures
2025 |
Iranzo-Sánchez, Jorge; Santamaría-Jordà, Jaume; Mas-Mollà, Gerard; Garcés Díaz-Munío, Gonçal V; Iranzo-Sánchez, Javier; Jorge, Javier; Silvestre-Cerdà, Joan Albert; Giménez, Adrià; Civera, Jorge; Sanchis, Albert; Juan, Alfons Speech Translation for Multilingual Medical Education Leveraged by Large Language Models Journal Article Artificial Intelligence In Medicine, 166 , pp. 103147, 2025. Abstract | Links | BibTeX | Tags: Automatic Speech Recognition, domain adaptation, large language models, Machine Translation, oncology, Speech Translation @article{Iranzo-Sánchez2025, title = {Speech Translation for Multilingual Medical Education Leveraged by Large Language Models}, author = {Jorge Iranzo-Sánchez AND Jaume Santamaría-Jordà AND Gerard Mas-Mollà AND Garcés Díaz-Munío, Gonçal V. AND Javier Iranzo-Sánchez AND Javier Jorge AND Joan Albert Silvestre-Cerdà AND Adrià Giménez AND Jorge Civera AND Albert Sanchis AND Alfons Juan}, doi = {10.1016/j.artmed.2025.103147}, year = {2025}, date = {2025-01-01}, journal = {Artificial Intelligence In Medicine}, volume = {166}, pages = {103147}, abstract = {The application of large language models (LLMs) to speech translation (ST), or in general, to machine translation (MT), has recently provided excellent results superseding conventional encoder-decoder MT systems in the general domain. However, this is not clearly the case when LLMs as MT systems are translating medical-related materials. In this respect, the provision of multilingual training materials for oncology professionals is a goal of the EU project Interact-Europe in which this work was framed. To this end, cross-language technology adapted to the oncology domain was developed, evaluated and deployed for multilingual interspeciality medical education. More precisely, automatic speech recognition (ASR) and MT models were adapted to the oncology domain to translate English pre-recorded training videos, kindly provided by the European School of Oncology (ESO), into French, Spanish, German and Slovene. In this work, three categories of MT models adapted to the medical domain were assessed: bilingual encoder-decoder MT models trained from scratch, pre-trained large multilingual encoder-decoder MT models and multilingual decoder-only LLMs. The experimental results underline the competitiveness in translation quality of LLMs compared to encoder-decoder MT models. Finally, the ESO speech dataset, comprising roughly 1,000 videos and 745 hours for the training and evaluation of ASR and MT models, was publicly released for the scientific community.}, keywords = {Automatic Speech Recognition, domain adaptation, large language models, Machine Translation, oncology, Speech Translation}, pubstate = {published}, tppubtype = {article} } The application of large language models (LLMs) to speech translation (ST), or in general, to machine translation (MT), has recently provided excellent results superseding conventional encoder-decoder MT systems in the general domain. However, this is not clearly the case when LLMs as MT systems are translating medical-related materials. In this respect, the provision of multilingual training materials for oncology professionals is a goal of the EU project Interact-Europe in which this work was framed. To this end, cross-language technology adapted to the oncology domain was developed, evaluated and deployed for multilingual interspeciality medical education. More precisely, automatic speech recognition (ASR) and MT models were adapted to the oncology domain to translate English pre-recorded training videos, kindly provided by the European School of Oncology (ESO), into French, Spanish, German and Slovene. In this work, three categories of MT models adapted to the medical domain were assessed: bilingual encoder-decoder MT models trained from scratch, pre-trained large multilingual encoder-decoder MT models and multilingual decoder-only LLMs. The experimental results underline the competitiveness in translation quality of LLMs compared to encoder-decoder MT models. Finally, the ESO speech dataset, comprising roughly 1,000 videos and 745 hours for the training and evaluation of ASR and MT models, was publicly released for the scientific community. |
2021 |
Iranzo-Sánchez, Javier; Jorge, Javier; Baquero-Arnal, Pau; Silvestre-Cerdà, Joan Albert ; Giménez, Adrià; Civera, Jorge; Sanchis, Albert; Juan, Alfons Streaming cascade-based speech translation leveraged by a direct segmentation model Journal Article Neural Networks, 142 , pp. 303–315, 2021. Abstract | Links | BibTeX | Tags: Automatic Speech Recognition, Cascade System, Deep Neural Networks, Hybrid System, Machine Translation, Segmentation Model, Speech Translation, streaming @article{Iranzo-Sánchez2021, title = {Streaming cascade-based speech translation leveraged by a direct segmentation model}, author = {Javier Iranzo-Sánchez and Javier Jorge and Pau Baquero-Arnal and Silvestre-Cerdà, Joan Albert and Adrià Giménez and Jorge Civera and Albert Sanchis and Alfons Juan}, doi = {10.1016/j.neunet.2021.05.013}, year = {2021}, date = {2021-01-01}, journal = {Neural Networks}, volume = {142}, pages = {303--315}, abstract = {The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. Nowadays, state-of-the-art ST systems are populated with deep neural networks that are conceived to work in an offline setup in which the audio input to be translated is fully available in advance. However, a streaming setup defines a completely different picture, in which an unbounded audio input gradually becomes available and at the same time the translation needs to be generated under real-time constraints. In this work, we present a state-of-the-art streaming ST system in which neural-based models integrated in the ASR and MT components are carefully adapted in terms of their training and decoding procedures in order to run under a streaming setup. In addition, a direct segmentation model that adapts the continuous ASR output to the capacity of simultaneous MT systems trained at the sentence level is introduced to guarantee low latency while preserving the translation quality of the complete ST system. The resulting ST system is thoroughly evaluated on the real-life streaming Europarl-ST benchmark to gauge the trade-off between quality and latency for each component individually as well as for the complete ST system.}, keywords = {Automatic Speech Recognition, Cascade System, Deep Neural Networks, Hybrid System, Machine Translation, Segmentation Model, Speech Translation, streaming}, pubstate = {published}, tppubtype = {article} } The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. Nowadays, state-of-the-art ST systems are populated with deep neural networks that are conceived to work in an offline setup in which the audio input to be translated is fully available in advance. However, a streaming setup defines a completely different picture, in which an unbounded audio input gradually becomes available and at the same time the translation needs to be generated under real-time constraints. In this work, we present a state-of-the-art streaming ST system in which neural-based models integrated in the ASR and MT components are carefully adapted in terms of their training and decoding procedures in order to run under a streaming setup. In addition, a direct segmentation model that adapts the continuous ASR output to the capacity of simultaneous MT systems trained at the sentence level is introduced to guarantee low latency while preserving the translation quality of the complete ST system. The resulting ST system is thoroughly evaluated on the real-life streaming Europarl-ST benchmark to gauge the trade-off between quality and latency for each component individually as well as for the complete ST system. |
2020 |
Iranzo-Sánchez, Javier; Silvestre-Cerdà, Joan Albert; Jorge, Javier; Roselló, Nahuel; Giménez, Adrià; Sanchis, Albert; Civera, Jorge; Juan, Alfons Europarl-ST: A Multilingual Corpus for Speech Translation of Parliamentary Debates Inproceedings Proc. of 45th Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2020), pp. 8229–8233, Barcelona (Spain), 2020. Abstract | Links | BibTeX | Tags: Automatic Speech Recognition, Machine Translation, Multilingual Corpus, Speech Translation, Spoken Language Translation @inproceedings{Iranzo2020, title = {Europarl-ST: A Multilingual Corpus for Speech Translation of Parliamentary Debates}, author = {Javier Iranzo-Sánchez and Joan Albert Silvestre-Cerdà and Javier Jorge and Nahuel Roselló and Adrià Giménez and Albert Sanchis and Jorge Civera and Alfons Juan}, url = {https://arxiv.org/abs/1911.03167 https://paperswithcode.com/paper/europarl-st-a-multilingual-corpus-for-speech https://www.mllp.upv.es/europarl-st/}, doi = {10.1109/ICASSP40776.2020.9054626}, year = {2020}, date = {2020-01-01}, booktitle = {Proc. of 45th Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2020)}, pages = {8229--8233}, address = {Barcelona (Spain)}, abstract = {Current research into spoken language translation (SLT), or speech-to-text translation, is often hampered by the lack of specific data resources for this task, as currently available SLT datasets are restricted to a limited set of language pairs. In this paper we present Europarl-ST, a novel multilingual SLT corpus containing paired audio-text samples for SLT from and into 6 European languages, for a total of 30 different translation directions. This corpus has been compiled using the de-bates held in the European Parliament in the period between2008 and 2012. This paper describes the corpus creation process and presents a series of automatic speech recognition,machine translation and spoken language translation experiments that highlight the potential of this new resource. The corpus is released under a Creative Commons license and is freely accessible and downloadable.}, keywords = {Automatic Speech Recognition, Machine Translation, Multilingual Corpus, Speech Translation, Spoken Language Translation}, pubstate = {published}, tppubtype = {inproceedings} } Current research into spoken language translation (SLT), or speech-to-text translation, is often hampered by the lack of specific data resources for this task, as currently available SLT datasets are restricted to a limited set of language pairs. In this paper we present Europarl-ST, a novel multilingual SLT corpus containing paired audio-text samples for SLT from and into 6 European languages, for a total of 30 different translation directions. This corpus has been compiled using the de-bates held in the European Parliament in the period between2008 and 2012. This paper describes the corpus creation process and presents a series of automatic speech recognition,machine translation and spoken language translation experiments that highlight the potential of this new resource. The corpus is released under a Creative Commons license and is freely accessible and downloadable. |
Iranzo-Sánchez, Javier; Giménez Pastor, Adrià ; Silvestre-Cerdà, Joan Albert; Baquero-Arnal, Pau; Saiz, Jorge Civera; Juan, Alfons Direct Segmentation Models for Streaming Speech Translation Inproceedings Proc. of 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020), pp. 2599–2611, 2020. Abstract | Links | BibTeX | Tags: Segmentation, Speech Translation, streaming @inproceedings{Iranzo-Sánchez2020, title = {Direct Segmentation Models for Streaming Speech Translation}, author = {Javier Iranzo-Sánchez and Giménez Pastor, Adrià and Joan Albert Silvestre-Cerdà and Pau Baquero-Arnal and Jorge Civera Saiz and Alfons Juan}, url = {http://dx.doi.org/10.18653/v1/2020.emnlp-main.206}, year = {2020}, date = {2020-01-01}, booktitle = {Proc. of 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)}, pages = {2599--2611}, abstract = {The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. These systems are usually connected by a segmenter that splits the ASR output into, hopefully, semantically self-contained chunks to be fed into the MT system. This is especially challenging in the case of streaming ST, where latency requirements must also be taken into account. This work proposes novel segmentation models for streaming ST that incorporate not only textual, but also acoustic information to decide when the ASR output is split into a chunk. An extensive and thorough experimental setup is carried out on the Europarl-ST dataset to prove the contribution of acoustic information to the performance of the segmentation model in terms of BLEU score in a streaming ST scenario. Finally, comparative results with previous work also show the superiority of the segmentation models proposed in this work.}, keywords = {Segmentation, Speech Translation, streaming}, pubstate = {published}, tppubtype = {inproceedings} } The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. These systems are usually connected by a segmenter that splits the ASR output into, hopefully, semantically self-contained chunks to be fed into the MT system. This is especially challenging in the case of streaming ST, where latency requirements must also be taken into account. This work proposes novel segmentation models for streaming ST that incorporate not only textual, but also acoustic information to decide when the ASR output is split into a chunk. An extensive and thorough experimental setup is carried out on the Europarl-ST dataset to prove the contribution of acoustic information to the performance of the segmentation model in terms of BLEU score in a streaming ST scenario. Finally, comparative results with previous work also show the superiority of the segmentation models proposed in this work. |