Enhancing the Podcast Browsing Experience through Topic Segmentation and Visualization with Generative AI

Podcasts present challenges in information retrieval due to their non-visual nature and extended length. To understand these challenges, we conducted interviews with 12 podcast users and identified difficulties in grasping the overall podcast content with metadata alone, highlighting the necessity of navigating to specific segments. Based on this finding, we propose a browsing method that utilizes Large Language Models (LLMs) and image generation models to segment podcast contents, integrating visual cues for supporting efficient navigation. To investigate how this new method differs from conventional approaches and to evaluate its effectiveness, we conducted another user study with 12 participants. The results revealed that keyword search is ineffective when dealing with unfamiliar or inaccurate keywords. Additionally, it requires thorough examination of the script to comprehend the overall content of each episode. On the other hand, segmenting the contents and labeling the topic for each segment facilitated was found to be helpful for understanding of the overall content, enabling easy navigation to desired topics. Furthermore, we found that providing an image enabled participants to easily distinguish one segment from another, which was preferred by participants. This multimodal browsing approach is expected to establish a foundational framework for the effective browsing and comprehension of audio content, extending its applicability beyond podcasts to various forms of audio files.