100,000 Podcasts: A Spoken English Document Corpus Ann Clifton Sravana Reddy Yongze Yu Spotify Spotify Spotify
[email protected] [email protected] [email protected] Aasish Pappu Rezvaneh Rezapour∗ Hamed Bonab∗ Spotify University of Illinois University of Massachusetts
[email protected] at Urbana-Champaign Amherst
[email protected] [email protected] Maria Eskevich Gareth J. F. Jones Jussi Karlgren CLARIN ERIC Dublin City University Spotify
[email protected] [email protected] [email protected] Ben Carterette Rosie Jones Spotify Spotify
[email protected] [email protected] Abstract Podcasts are a large and growing repository of spoken audio. As an audio format, podcasts are more varied in style and production type than broadcast news, contain more genres than typi- cally studied in video data, and are more varied in style and format than previous corpora of conversations. When transcribed with automatic speech recognition they represent a noisy but fascinating collection of documents which can be studied through the lens of natural language processing, information retrieval, and linguistics. Paired with the audio files, they are also a re- source for speech processing and the study of paralinguistic, sociolinguistic, and acoustic aspects of the domain. We introduce the Spotify Podcast Dataset, a new corpus of 100,000 podcasts. We demonstrate the complexity of the domain with a case study of two tasks: (1) passage search and (2) summarization. This is orders of magnitude larger than previous speech corpora used for search and summarization. Our results show that the size and variability of this corpus opens up new avenues for research.