PodCastle: A Spoken Document Retrieval Service Improved by Anonymous User Contributions

In this invited paper, we introduce a public web service, PodCastle, that provides full-text searching of speech data (Japanese podcasts) on the basis of automatic speech recognition technologies. This is an instance of our research approach, Speech Recognition Research 2.0, which is aimed at providing users with a web service based on Web 2.0 so that they can experience state-of-the-art speech recognition performance, and at promoting speech recognition technologies in cooperation with anonymous users. PodCastle enables users to find podcasts 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. Even if a state-of-the-art speech recognizer is used to recognize podcasts on the web, a number of errors will naturally occur. PodCastle therefore encourages users to cooperate by correcting these errors so that those podcasts can be searched more reliably. Furthermore, using the resulting corrections to train the speech recognizer, it implements a mechanism whereby the speech recognition performance is gradually improved. In our experiences from its practical use over the past 46 months (since December, 2006), we confirmed that the performance of PodCastle was improved by a number of anonymous user contributions.