Accessibility Automatic Speech Recognition Computer-assisted transcription Confidence measures Docencia en Red Education language model adaptation Language Modeling Language Technologies Length modelling Log-linear models Machine Translation Massive Adaptation Modelat de la longitud 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
2018 |
Valor Miró, Juan Daniel ; Baquero-Arnal, Pau; Civera, Jorge; Turró, Carlos; Juan, Alfons Multilingual videos for MOOCs and OER Journal Article Journal of Educational Technology & Society, 21 (2), pp. 1–12, 2018. Abstract | Links | BibTeX | Tags: Machine Translation, MOOCs, multilingual, Speech Recognition, video lecture repositories @article{Miró2018, title = {Multilingual videos for MOOCs and OER}, author = {Valor Miró, Juan Daniel and Pau Baquero-Arnal and Jorge Civera and Carlos Turró and Alfons Juan}, url = {https://www.mllp.upv.es/wp-content/uploads/2019/11/JETS2018MLLP.pdf http://hdl.handle.net/10251/122577 https://www.jstor.org/stable/26388375 https://www.j-ets.net/collection/published-issues/21_2}, year = {2018}, date = {2018-01-01}, journal = {Journal of Educational Technology & Society}, volume = {21}, number = {2}, pages = {1--12}, abstract = {Massive Open Online Courses (MOOCs) and Open Educational Resources (OER) are rapidly growing, but are not usually offered in multiple languages due to the lack of cost-effective solutions to translate the different objects comprising them and particularly videos. However, current state-of-the-art automatic speech recognition (ASR) and machine translation (MT) techniques have reached a level of maturity which opens the possibility of producing multilingual video subtitles of publishable quality at low cost. This work summarizes authors' experience in exploring this possibility in two real-life case studies: a MOOC platform and a large video lecture repository. Apart from describing the systems, tools and integration components employed for such purpose, a comprehensive evaluation of the results achieved is provided in terms of quality and efficiency. More precisely, it is shown that draft multilingual subtitles produced by domain-adapted ASR/MT systems reach a level of accuracy that make them worth post-editing, instead of generating them ex novo, saving approximately 25%–75% of the time. Finally, the results reported on user multilingual data consumption reflect that multilingual subtitles have had a very positive impact in our case studies boosting student enrolment, in the case of the MOOC platform, by 70% relative.}, keywords = {Machine Translation, MOOCs, multilingual, Speech Recognition, video lecture repositories}, pubstate = {published}, tppubtype = {article} } Massive Open Online Courses (MOOCs) and Open Educational Resources (OER) are rapidly growing, but are not usually offered in multiple languages due to the lack of cost-effective solutions to translate the different objects comprising them and particularly videos. However, current state-of-the-art automatic speech recognition (ASR) and machine translation (MT) techniques have reached a level of maturity which opens the possibility of producing multilingual video subtitles of publishable quality at low cost. This work summarizes authors' experience in exploring this possibility in two real-life case studies: a MOOC platform and a large video lecture repository. Apart from describing the systems, tools and integration components employed for such purpose, a comprehensive evaluation of the results achieved is provided in terms of quality and efficiency. More precisely, it is shown that draft multilingual subtitles produced by domain-adapted ASR/MT systems reach a level of accuracy that make them worth post-editing, instead of generating them ex novo, saving approximately 25%–75% of the time. Finally, the results reported on user multilingual data consumption reflect that multilingual subtitles have had a very positive impact in our case studies boosting student enrolment, in the case of the MOOC platform, by 70% relative. |
2015 |
Valor Miró, Juan Daniel ; Silvestre-Cerdà, Joan Albert; Civera, Jorge; Turró, Carlos; Juan, Alfons Efficiency and usability study of innovative computer-aided transcription strategies for video lecture repositories Journal Article Speech Communication, 74 , pp. 65–75, 2015, ISSN: 0167-6393. Abstract | Links | BibTeX | Tags: Automatic Speech Recognition, Computer-assisted transcription, Interface design strategies, Usability study, video lecture repositories @article{Valor201565, title = {Efficiency and usability study of innovative computer-aided transcription strategies for video lecture repositories}, author = {Valor Miró, Juan Daniel and Joan Albert Silvestre-Cerdà and Jorge Civera and Carlos Turró and Alfons Juan}, url = {http://www.sciencedirect.com/science/article/pii/S0167639315001016 http://www.mllp.upv.es/wp-content/uploads/2016/03/paper1.pdf}, issn = {0167-6393}, year = {2015}, date = {2015-01-01}, journal = {Speech Communication}, volume = {74}, pages = {65--75}, abstract = {Abstract Video lectures are widely used in education to support and complement face-to-face lectures. However, the utility of these audiovisual assets could be further improved by adding subtitles that can be exploited to incorporate added-value functionalities such as searchability, accessibility, translatability, note-taking, and discovery of content-related videos, among others. Today, automatic subtitles are prone to error, and need to be reviewed and post-edited in order to ensure that what students see on-screen are of an acceptable quality. This work investigates different user interface design strategies for this post-editing task to discover the best way to incorporate automatic transcription technologies into large educational video repositories. Our three-phase study involved lecturers from the Universitat Politècnica de València (UPV) with videos available on the poliMedia video lecture repository, which is currently over 10,000 video objects. Simply by conventional post-editing automatic transcriptions users almost reduced to half the time that would require to generate the transcription from scratch. As expected, this study revealed that the time spent by lecturers reviewing automatic transcriptions correlated directly with the accuracy of said transcriptions. However, it is also shown that the average time required to perform each individual editing operation could be precisely derived and could be applied in the definition of a user model. In addition, the second phase of this study presents a transcription review strategy based on confidence measures (CM) and compares it to the conventional post-editing strategy. Finally, a third strategy resulting from the combination of that based on \\{CM\\} with massive adaptation techniques for automatic speech recognition (ASR), achieved to improve the transcription review efficiency in comparison with the two aforementioned strategies.}, keywords = {Automatic Speech Recognition, Computer-assisted transcription, Interface design strategies, Usability study, video lecture repositories}, pubstate = {published}, tppubtype = {article} } Abstract Video lectures are widely used in education to support and complement face-to-face lectures. However, the utility of these audiovisual assets could be further improved by adding subtitles that can be exploited to incorporate added-value functionalities such as searchability, accessibility, translatability, note-taking, and discovery of content-related videos, among others. Today, automatic subtitles are prone to error, and need to be reviewed and post-edited in order to ensure that what students see on-screen are of an acceptable quality. This work investigates different user interface design strategies for this post-editing task to discover the best way to incorporate automatic transcription technologies into large educational video repositories. Our three-phase study involved lecturers from the Universitat Politècnica de València (UPV) with videos available on the poliMedia video lecture repository, which is currently over 10,000 video objects. Simply by conventional post-editing automatic transcriptions users almost reduced to half the time that would require to generate the transcription from scratch. As expected, this study revealed that the time spent by lecturers reviewing automatic transcriptions correlated directly with the accuracy of said transcriptions. However, it is also shown that the average time required to perform each individual editing operation could be precisely derived and could be applied in the definition of a user model. In addition, the second phase of this study presents a transcription review strategy based on confidence measures (CM) and compares it to the conventional post-editing strategy. Finally, a third strategy resulting from the combination of that based on \{CM\} with massive adaptation techniques for automatic speech recognition (ASR), achieved to improve the transcription review efficiency in comparison with the two aforementioned strategies. |