Performing Automated Speech Transcription for Enhancing Learning – PASTEL

Along with the democratization of increasingly high-performance digital and communication technologies, higher education and training for adults are constantly challenged by both the renewal and the adaptation of teaching practices. While the frontiers between guided learning and self-learning are becoming less obvious, which tends to redefine the role of the teacher and the learner, the great accessibility of technologies, on the other hand, enables a diversity of interaction modes between teachers and learners, as well as between learners and learners.

We believe that the widespread use of digital technologies, especially online courses starts with the development of SPOC (Small Private Online Courses) at a reduced cost while capable of largely covering numerous educational areas. For that matter, the engineering process needs to better involve teachers in charge of the lectures, and to allow them to personalize their content and teaching methods in order to develop blended learning, thus the entanglement of the use of digital content and classroom teaching.

PASTEL is a research project that aims to explore the potential of real time and automatic transcriptions for the instrumentation of mixed educational situations where the modalities of the interactions can be face-to-face or online, synchronous or asynchronous. The speech recognition technology approaches a maturity level that allows new opportunities of instrumentation in pedagogical practices and their new uses. More specifically, we develop (1) a real-time transcription application, and (2) educational outreach applications based on the transcription system outputs. We will use these results to automatically generate the materials of a basic SPOC. A set of editing features will be implemented for the mentioned applications that will allow the teacher to adapt and customize these contents according to their needs. Then, the developed applications will be made available to public institutions for higher education and research, and will also be transferred to the industry through Orange or start-ups associated to the research laboratories involved in the project.

The major innovations of PASTEL cover the discourse structure from automatic transcriptions that are linked to its educational objectives. The innovation also features the challenging flow processing in real time, which is required when the discourse structure is being used in a face-to-face situation. The project also brings innovative solutions in terms of instrumentation, and diversification of pedagogical practices, as well as a new approach to design and structure online educational contents, based on the use of speech recognition technology.