Navigating Speech Recording Collections with AI-Generated Illustrations
This addresses the problem of information extraction from speech archives for users, though it appears incremental as it builds on existing generative models.
The paper tackles the challenge of navigating large speech collections by proposing a method that uses AI-generated illustrations and interactive mind maps, demonstrating it on the TED-LIUM~3 dataset with initial user tests showing potential to simplify exploration.
Although the amount of available spoken content is steadily increasing, extracting information and knowledge from speech recordings remains challenging. Beyond enhancing traditional information retrieval methods such as speech search and keyword spotting, novel approaches for navigating and searching spoken content need to be explored and developed. In this paper, we propose a novel navigational method for speech archives that leverages recent advances in language and multimodal generative models. We demonstrate our approach with a Web application that organizes data into a structured format using interactive mind maps and image generation tools. The system is implemented using the TED-LIUM~3 dataset, which comprises over 2,000 speech transcripts and audio files of TED Talks. Initial user tests using a System Usability Scale (SUS) questionnaire indicate the application's potential to simplify the exploration of large speech collections.