Memad project: end user feedback on ai in the media production workflows

This paper discusses the prototypes built and end-user trials run in the European H2020 project MeMAD (Methods for Managing Audiovisual Data) for implementing more efficient media production based on semiautomated media enrichment tools. The prototypes offer automated content annotation supported by machine translation, cross-language search and retrieval of material and automated multi-lingual video subtitling. Alternative evaluation approaches are described for experimental and close-to-production stage use cases, with the focus alternatively on refining the use cases with qualitative methods or measuring productivity with quantitative methods. Main findings indicate curious user attitudes towards these types of technologies, with current working practices and individual preferences affecting the results quite strongly. Productivity of subtitling and translation work can be improved by incorporating automated speech recognition (ASR), natural language processing (NLP) and machine translation into the workflows. Using large quantities of metadata raises tool UX design questions and is not fully supported by existing tools. For most purposes tested, the users preferred having the additional metadata available, even in lower quality, instead of hiding or discarding low-quality data.