Speech2Mindmap: Testing the Accuracy of Unsupervised Automatic Mindmapping Technology With Speech Recognition

This research aims to augment human cognition through the advancement and automation of mindmapping technologies, which could later support human creativity and virtual collaboration. Mindmapping is a visual brainstorming technique that allows problem solvers to utilize the human brain's ability to retrieve knowledge through similarity and association. While it is a powerful tool to generate concepts in any phase of s or design, the content of mindmaps is usually manually generated while listening or conversing and generating ideas, requiring a high cognitive load. This work introduces the development of a speech-driven automated mindmapping technology, called Speech2Mindmap. The specifics of the Speech2Mindmap algorithm are detailed, along with two case studies that serve to test its accuracy in comparison to human-generated mindmaps, using audio recorded speech data as input. In the first case study, the Speech2Mindmap algorithm was evaluated on how well it represents manually generated human mindmapping output. The second case study evaluated the reliability of the Speech2Mindmap algorithm and examined the best performing methods and conditions to achieve the greatest similarity to human-generated mindmaps. This research demonstrates that the Speech2Mindmap algorithm is capable of representing manually generated human mindmapping output and found the best performing methods and conditions to generate a mindmap that is 80% similar, on average, to human-generated mindmaps.