PodCastle and songle: crowdsourcing-based web services for spoken content retrieval and active music listening

  • Authors:

  • Masataka Goto;Jun Ogata;Kazuyoshi Yoshii;Hiromasa Fujihara;Matthias Mauch;Tomoyasu Nakano

  • Affiliations:

  • National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan;National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan;National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan;National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan;National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan;National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan

  • Venue:
  • Proceedings of the ACM multimedia 2012 workshop on Crowdsourcing for multimedia
  • Year:
  • 2012

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Abstract

In this keynote talk, we describe two crowdsourcing-based web services, PodCastle (http://en.podcastle.jp for the English version and http://podcastle.jp for the Japanese version) and Songle (http://songle.jp). PodCastle and Songle collect voluntary contributions by anonymous users in order to improve the experiences of users listening to speech and music content available on the web. These services use automatic speech-recognition and music-understanding technologies to provide content analysis results, such as full-text speech transcriptions and music scene descriptions, that let users enjoy content-based multimedia retrieval and active browsing of speech and music signals without relying on metadata. When automatic content analysis is used, however, errors are inevitable. PodCastle and Songle therefore provide an efficient error correction interface that let users easily correct errors by selecting from a list of candidate alternatives. Through these corrections, users gain a real sense of contributing for their own benefit and that of others and can be further motivated to contribute by seeing corrections made by other users. Our services promote the popularization and use of speech-recognition and music-understanding technologies by raising user awareness. Users can grasp the nature of those technologies just by seeing results obtained when the technologies applied to speech data and songs available on the web.