Implementation of automation tools based on machine learning and artificial intelligence, what future does AI hold for audiovisual librarians, what role can they play and how to manage such a big change?
In 2015, to deal with the challenges raised by the massification of digital data, INA has embarked on a vast project to completely overhaul its information systems.
Embracing a data centric strategy, this overhaul meets the necessary conditions for the implementation of automation tools based on machine learning and artificial intelligence to segment and describe audiovisual content. After identifying a few use cases, the works to automate segmentation and indexing media process was launch in 2019. These technical and material issues come with human and professional challenges, asking for specific business user’s skills and knowledges.
This presentation aims to give a few answers, at least advices and feedbacks, to a simple question: what future does AI hold for audiovisual librarians, what role can they play and how to manage such a big change?
Regional Languages with Machine Learning: Achievements and Challenges (small languages and dialects). Radiotelevisiun Svizra Rumantscha (RTR, SRG SSR) has launched a project for the written reproduction of spoken Romansh
The Romansh media company Radiotelevisiun Svizra Rumantscha (RTR, SRG SSR) has launched a project for the written reproduction of spoken Romansh. In order to be able to teach Romansh to the computer at all, RTR first had to acquire data to feed the system to learn Romansh. Bernard Bearth, Head of Data and Archives at RTR, explains the value of the existing archives for this project and the importance of speech to text and automatic translation for RTR archives in the future.
Regional Languages with Machine Learning: Achievements and Challenges University of Edinburgh : Gaelic Speech Recognition: Challenges Faced, Lessons Learnt and Future Plans
In this seminar, Dr Will Lamb (University of Edinburgh) will discuss work towards an accurate, general purpose Automatic Speech Recognition (ASR) system for Scottish Gaelic. In 2021, a team of researchers at Edinburgh developed the first working example of such as system, with assistance from MG Alba and several other non-academic partners. By the end of the project, the system achieved an accuracy level of approximately 73%. While this is a far cry from state-of-the-art English systems, used in applications such as Siri and Alexa, it is a promising beginning and confirms the methodology adopted. Modern ASR systems are machine-learning based, and trained upon tens of thousands, if not millions, of hours of audio and corresponding text. The principal challenge facing minority languages, such as Gaelic, is acquiring suitable training data. In this seminar, Dr Lamb will outline plans to augment the current 103 hours of data fiftyfold (i.e. to 5000 hours). This increase would translate to a much more accurate and robust system. The goal is to develop a subtitling system appropriate for broadcasting purposes in the next 3 to 4 years.