Efficient Generation of High-Quality Multilingual Subtitles for Video Lecture Repositories
Video lectures are a valuable educational tool in higher education to support or replace face-to-face lectures in active learning strategies. In 2007 the Universitat Politècnica de València (UPV) implemented its video lecture capture system, resulting in
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stract. Video lectures are a valuable educational tool in higher education to support or replace face-to-face lectures in active learning strategies. In 2007 the Universitat Polit`ecnica de Val`encia (UPV) implemented its video lecture capture system, resulting in a high quality educational video repository, called poliMedia, with more than 10.000 mini lectures created by 1.373 lecturers. Also, in the framework of the European project transLectures, UPV has automatically generated transcriptions and translations in Spanish, Catalan and English for all videos included in the poliMedia video repository. transLectures’s objective responds to the widely-recognised need for subtitles to be provided with video lectures, as an essential service for non-native speakers and hearing impaired persons, and to allow advanced repository functionalities. Although high-quality automatic transcriptions and translations were generated in transLectures, they were not error-free. For this reason, lecturers need to manually review video subtitles to guarantee the absence of errors. The aim of this study is to evaluate the efficiency of the manual review process from automatic subtitles in comparison with the conventional generation of video subtitles from scratch. The reported results clearly indicate the convenience of providing automatic subtitles as a first step in the generation of video subtitles and the significant savings in time of up to almost 75 % involved in reviewing subtitles. Keywords: Video lecture repositories · Automatic speech recognition Machine translation · Efficient video subtitling
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Introduction
Video lectures are fast becoming an everyday educational resource in higher education used to supplement and complement face-to-face lectures [6], and are being incorporated into existing university curricula around the world with enthusiastic response from students [8]. However, the utility of these audiovisual assets could be further extended by adding subtitles that can be exploited to incorporate added-value functionalities such as searchability, accessibility, and discovery of content-related videos, c Springer International Publishing Switzerland 2015 G. Conole et al. (Eds.): EC-TEL 2015, LNCS 9307, pp. 485–490, 2015. DOI: 10.1007/978-3-319-24258-3 44
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among others. In fact, most of the video lectures available in university large repositories are neither transcribed nor translated, despite the clear need to make their content accessible to speakers of different languages and people with disabilities ([10]). Also, the subtitles can be used to develop advanced educational functionalities like content summarisation to assist student note-taking ([2]). For this reason, it is important to develop a cost-effective solution that can do so to a reasonable degree of accuracy. In this work, we propose the application of state-of-the-art techniques in Automatic Speech Recognition (ASR) and Statistical Machine Translation (SMT) to generate high-quality video subtitles under the supervision of lec
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