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 Models log-lineals Multilingualism Neural Machine Translation Opencast Matterhorn Polimedia Sliding window Speaker adaptation Speech Recognition Speech Translation Statistical machine translation streaming text-to-speech transcripciones video lecture repositories Video Lectures
2022 |
Pérez González de Martos, Alejandro Deep Neural Networks for Automatic Speech-To-Speech Translation of Open Educational Resources PhD Thesis Universitat Politècnica de València, 2022, (Advisors: Alfons Juan Ciscar and Alberto Sanchis Navarro). Links | BibTeX | Tags: automatic dubbing, cross-lingual voice cloning, educational resources, simultaneous machine interpretation, text-to-speech @phdthesis{aperez2022, title = {Deep Neural Networks for Automatic Speech-To-Speech Translation of Open Educational Resources}, author = {Pérez González de Martos, Alejandro}, url = {http://hdl.handle.net/10251/184019}, doi = {10.4995/Thesis/10251/184019}, year = {2022}, date = {2022-06-15}, school = {Universitat Politècnica de València}, note = {Advisors: Alfons Juan Ciscar and Alberto Sanchis Navarro}, keywords = {automatic dubbing, cross-lingual voice cloning, educational resources, simultaneous machine interpretation, text-to-speech}, pubstate = {published}, tppubtype = {phdthesis} } |
Pérez González de Martos, Alejandro ; Giménez Pastor, Adrià ; Jorge Cano, Javier ; Iranzo-Sánchez, Javier; Silvestre-Cerdà, Joan Albert; Garcés Díaz-Munío, Gonçal V; Baquero-Arnal, Pau; Sanchis Navarro, Alberto ; Civera Sáiz, Jorge ; Juan Ciscar, Alfons ; Turró Ribalta, Carlos Doblaje automático de vídeo-charlas educativas en UPV[Media] Inproceedings Proc. of VIII Congrés d'Innovació Educativa i Docència en Xarxa (IN-RED 2022), pp. 557–570, València (Spain), 2022. Abstract | Links | BibTeX | Tags: automatic dubbing, Automatic Speech Recognition, Machine Translation, OER, text-to-speech @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} } 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. |
2021 |
Pérez, Alejandro; Garcés Díaz-Munío, Gonçal ; Giménez, Adrià; Silvestre-Cerdà, Joan Albert ; Sanchis, Albert; Civera, Jorge; Jiménez, Manuel; Turró, Carlos; Juan, Alfons Towards cross-lingual voice cloning in higher education Journal Article Engineering Applications of Artificial Intelligence, 105 , pp. 104413, 2021. Abstract | Links | BibTeX | Tags: cross-lingual voice conversion, educational resources, multilinguality, OER, text-to-speech @article{Pérez2021, title = {Towards cross-lingual voice cloning in higher education}, author = {Alejandro Pérez and Garcés Díaz-Munío, Gonçal and Adrià Giménez and Silvestre-Cerdà, Joan Albert and Albert Sanchis and Jorge Civera and Manuel Jiménez and Carlos Turró and Alfons Juan}, url = {https://doi.org/10.1016/j.engappai.2021.104413}, year = {2021}, date = {2021-10-01}, journal = {Engineering Applications of Artificial Intelligence}, volume = {105}, pages = {104413}, abstract = {The rapid progress of modern AI tools for automatic speech recognition and machine translation is leading to a progressive cost reduction to produce publishable subtitles for educational videos in multiple languages. Similarly, text-to-speech technology is experiencing large improvements in terms of quality, flexibility and capabilities. In particular, state-of-the-art systems are now capable of seamlessly dealing with multiple languages and speakers in an integrated manner, thus enabling lecturer's voice cloning in languages she/he might not even speak. This work is to report the experience gained on using such systems at the Universitat Politècnica de València (UPV), mainly as a guidance for other educational organizations willing to conduct similar studies. It builds on previous work on the UPV's main repository of educational videos, MediaUPV, to produce multilingual subtitles at scale and low cost. Here, a detailed account is given on how this work has been extended to also allow for massive machine dubbing of MediaUPV. This includes collecting 59 hours of clean speech data from UPV’s academic staff, and extending our production pipeline of subtitles with a state-of-the-art multilingual and multi-speaker text-to-speech system trained from the collected data. Our main result comes from an extensive, subjective evaluation of this system by lecturers contributing to data collection. In brief, it is shown that text-to-speech technology is not only mature enough for its application to MediaUPV, but also needed as soon as possible by students to improve its accessibility and bridge language barriers.}, keywords = {cross-lingual voice conversion, educational resources, multilinguality, OER, text-to-speech}, pubstate = {published}, tppubtype = {article} } The rapid progress of modern AI tools for automatic speech recognition and machine translation is leading to a progressive cost reduction to produce publishable subtitles for educational videos in multiple languages. Similarly, text-to-speech technology is experiencing large improvements in terms of quality, flexibility and capabilities. In particular, state-of-the-art systems are now capable of seamlessly dealing with multiple languages and speakers in an integrated manner, thus enabling lecturer's voice cloning in languages she/he might not even speak. This work is to report the experience gained on using such systems at the Universitat Politècnica de València (UPV), mainly as a guidance for other educational organizations willing to conduct similar studies. It builds on previous work on the UPV's main repository of educational videos, MediaUPV, to produce multilingual subtitles at scale and low cost. Here, a detailed account is given on how this work has been extended to also allow for massive machine dubbing of MediaUPV. This includes collecting 59 hours of clean speech data from UPV’s academic staff, and extending our production pipeline of subtitles with a state-of-the-art multilingual and multi-speaker text-to-speech system trained from the collected data. Our main result comes from an extensive, subjective evaluation of this system by lecturers contributing to data collection. In brief, it is shown that text-to-speech technology is not only mature enough for its application to MediaUPV, but also needed as soon as possible by students to improve its accessibility and bridge language barriers. |
Pérez-González-de-Martos, Alejandro; Sanchis, Albert; Juan, Alfons VRAIN-UPV MLLP's system for the Blizzard Challenge 2021 Inproceedings Proc. of Blizzard Challenge 2021, 2021. Abstract | Links | BibTeX | Tags: Blizzard Challenge, HiFi-GAN, text-to-speech @inproceedings{Pérez-González-de-Martos2021b, title = {VRAIN-UPV MLLP's system for the Blizzard Challenge 2021}, author = {Alejandro Pérez-González-de-Martos and Albert Sanchis and Alfons Juan}, url = {http://hdl.handle.net/10251/192554 https://arxiv.org/abs/2110.15792 http://www.festvox.org/blizzard/blizzard2021.html}, year = {2021}, date = {2021-01-01}, booktitle = {Proc. of Blizzard Challenge 2021}, abstract = {This paper presents the VRAIN-UPV MLLP’s speech synthesis system for the SH1 task of the Blizzard Challenge 2021. The SH1 task consisted in building a Spanish text-to-speech system trained on (but not limited to) the corpus released by the Blizzard Challenge 2021 organization. It included 5 hours of studio-quality recordings from a native Spanish female speaker. In our case, this dataset was solely used to build a two-stage neural text-to-speech pipeline composed of a non-autoregressive acoustic model with explicit duration modeling and a HiFi-GAN neural vocoder. Our team is identified as J in the evaluation results. Our system obtained very good results in the subjective evaluation tests. Only one system among other 11 participants achieved better naturalness than ours. Concretely, it achieved a naturalness MOS of 3.61 compared to 4.21 for real samples.}, keywords = {Blizzard Challenge, HiFi-GAN, text-to-speech}, pubstate = {published}, tppubtype = {inproceedings} } This paper presents the VRAIN-UPV MLLP’s speech synthesis system for the SH1 task of the Blizzard Challenge 2021. The SH1 task consisted in building a Spanish text-to-speech system trained on (but not limited to) the corpus released by the Blizzard Challenge 2021 organization. It included 5 hours of studio-quality recordings from a native Spanish female speaker. In our case, this dataset was solely used to build a two-stage neural text-to-speech pipeline composed of a non-autoregressive acoustic model with explicit duration modeling and a HiFi-GAN neural vocoder. Our team is identified as J in the evaluation results. Our system obtained very good results in the subjective evaluation tests. Only one system among other 11 participants achieved better naturalness than ours. Concretely, it achieved a naturalness MOS of 3.61 compared to 4.21 for real samples. |