100,000 Podcasts: A Spoken E nglish Document Corpus (2020)

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 typically 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 resource 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.

## introduction podcasts come in many formats and levels of formality. episodes appear on a regular or irregular cadence. they can be formal news journalism or conversational chat; fiction or non-fiction. they are sharply growing in popularity @xcite ) and yet have been relatively little studied. this medium opens up a rich palette of questions and issues for research in speech and language technology, linguistics, information access, and media studies. to facilitate research into podcasts, we have produced a corpus of podcast episodes, comprising nearly 60,000 hours of speech. this is orders of magnitude larger than previous transcribed speech datasets, and contains a rich variety of genres, subject matter, speaking styles, and structural formats. our contributions are four-fold: ## related datasets earlier speech corpora contained relatively clean audio, often with a single speaker reading from a prepared text, such as the timit collection @xcite or broadcast news corpora, which have been used as data sets for speech retrieval experiments in both trec @xcite and clef @xcite , and for topic detection and tracking @xcite . these more formal settings or samples of formal content are useful for the study of acoustic qualities of human speech, but represent a more idealized scenario than practical audio processing tasks of interest today. conversational datasets with noisier speech have been collected for specific domains, often intended to capture regularities of some particular communication situation, such as the atis corpus of air travel information requests @xcite , meeting recordings @xcite , telephone conversations @xcite , and broadcast news @xcite . there are some collections of more naturally occurring conversational material such as the callhome corpus @xcite , the santa barbara corpus of spoken american english @xcite and the ted talks corpus @xcite . while some of the content in such collections share characteristics with podcast material, podcasts' combination of unscripted and spontaneously organised discourse in a conversational setting, with turntaking, interviews, stretches of monologue, argumentation, and the inclusion of other audio material including non-speech segments is not yet represented in any collection of spoken language available with transcripts for research purposes. for summarization corpora in particular, the cnn/dailymail data @xcite is one of the few large summarization datasets with manually written summaries. spoken document summaries are also available for the ami meeting corpus @xcite and the icsi meeting corpus @xcite , as well as corpora of lectures @xcite , and voicemail @xcite . @xcite collect and evaluate 217 hours of podcasts for query-biased extractive summarization. in recent work, @xcite train a model to reproduce full-length manual reports aligned with automatic speech recognition transcripts of meetings, and gholipour ghalandari et al. (2020) generate a corpus for multi-document summarization. ## data overview we have compiled the spotify podcast dataset, the first large scale corpus of podcast audio data with automatically generated transcripts. this corpus is drawn from a variety of creators, ranging from professional podcasters with high production value, to amateurs recording podcasts using an application on their mobile phone. the podcasts cover a wide range of topics including lifestyle & culture, storytelling, sports & recreation, news, health, documentary, and commentary. in addition, the content is delivered in a variety of structural formats, number of speakers, and levels of formality, some scripted, others improvised, and presented in the forms of narrative, conversation, or debate. besides search and summarization, this data is valuable for tasks such as document segmentation or dialog modeling, and will enable new avenues of speech and language technology research. our corpus consists of over 100,000 podcast episodes, consisting of nearly 60,000 hours of audio and accompanying transcripts, as well as metadata such as creator-provided descriptions. the data was initially provided in the the context of the trec podcast track @xcite . we now make it available for more general research use. ## search: spoken passage retrieval high-quality search of topical content of podcast episodes is challenging. existing podcast search engines index the available metadata fields for the podcast as well as textual descriptions of the show and episode @xcite . these descriptions often fail to cover the salient aspects of the content. improving and extending podcast search is limited by the availability of transcripts and the cost of automatic speech recognition. our case-study is for fixed-length segment retrieval: given an arbitrary query (a phrase, sentence or set of words), retrieve topically relevant segments from the data. these segments can then be used as a basis for topical retrieval, for visualization, or other downstream purposes @xcite . a segment, for the purposes of our benchmark, is a two-minute chunk with one minute query type description black hole image topical in may 2019 astronomers released the first-ever picture of a black hole. i would like to hear some conversations and educational discussion about the science of astronomy, black holes, and of the picture itself. story about riding a bird re-finding i remember hearing a podcast that had a story about a kid riding some kind of bird. i want to find it again. daniel ek interview known item someone told me about a podcast interview with daniel ek, ceo of spotify, about the founding and early days of spotify. i would like to find the show and episode that contains that interview. other interviews with ek are relevant as well. ## summarization automated document summarization is the task of condensing an input text into a much shorter form that preserves most of the salient information. this dataset presents several challenges: 1) the input documents are automatically transcribed, and thus subject to speech recognition errors, 2) the documents are frequently of a casual, conversational nature, with utterance fragments and disfluencies, and 3) the documents are significantly longer than typical summarization data. thus, this task is most closely related to prior work in spoken document summarization and long document summarization @xcite . ## conclusions and future work we have presented the first large-scale dataset of transcribed podcasts. with this we have given benchmarks for a passage retrieval and a summarization task, along with an analysis that highlights ways in which this widely-varying spoken domain presents challenges for natural language processing and information retrieval.