PodCastle: Recent Advances of a Spoken Document Retrieval Service Improved by Anonymous User Contributions

In this paper, we introduce recent advances of a speech retrieval web service, PodCastle, that collects and amplifies voluntary contributions by anonymous users. Our goal is to provide users with a public web service based on speech recognition and crowdsourcing so that they can experience state-of-the-art speech recognition performance through a useful service. PodCastle enables users to find speech data (such as podcasts and YouTube video clips) that include a search term, read full texts of their recognition results, and easily correct recognition errors by simply selecting from a list of candidates. The resulting corrections were used to improve both the speech retrieval and recognition performances. In our experiences from its practical use over the past four years (since December, 2006), over half a million recognition errors in about one hundred thousand speech data were corrected by anonymous users and we confirmed that the speech recognition performance of PodCastle was actually improved by those corrections.