This research aims to identify users' goals and strategies when searching for podcasts and their impact on the design of podcast retrieval technology. In particular, the paper seeks to explore the potential to address user goals with indexing based on podcast metadata and automatic speech recognition (ASR) transcripts.
The paper conducted a user study to obtain an overview of podcast search behaviour and goals, using a multi‐method approach of an online survey, a diary study, and contextual interviews. In a subsequent podcast retrieval experiment, the paper investigated the retrieval performance of the two choices of indexing features for search goals identified during the study.
The paper found that study participants used a variety of search strategies, partially influenced by available tools and their perceptions of these tools. Furthermore the experimental results revealed that retrieval using ASR transcripts performed significantly better than metadata‐based searching. However, a detailed result analysis suggested that the efficacy of the indexing methods was search‐goal dependent.
The research constitutes a step towards a future framework for investigating user needs and addressing them in an experimental set‐up. It was primarily qualitative and exploratory in nature.
Podcast search engines require evidence about suitable indexing methods in order to make an informed decision concerning whether it is worth the resources to generate speech recognition transcripts.
Systematic studies of podcast searching have not previously been reported. Investigations of this kind hold the potential to optimise podcast retrieval in the long term.
