The purpose of the Strategic Workshop on Information Retrieval in Lorne (SWIRL)1 is to explore the long-range issues of the information retrieval (IR) field, to recognise challenges that are on – or even over – the horizon, to build consensus on key challenges, and to disseminate the resulting information to the research community. The intent is that this description of open problems will help to inspire researchers and graduate students to address the questions and will provide funding agencies with data to focus and coordinate support for IR research. Date: 10–12 February 2025. Website: https://sites.google.com/view/swirl2025/home.

Music, podcasts and audiobooks are rich audio content types with strong listener engagement. Search and recommendation across these content types can be greatly enhanced with a deep understanding of their content; across audio, text, and other multimodal content. In this talk, I discuss some of the challenges and opportunities in understanding this content. This deep understanding of content enables us to delight our users and expand the reach of our content creators. As part of enabling wider academic research into podcast content understanding, Spotify Research [1] has released a podcast dataset [2] with 120,000 hours of podcasts in English [3] and Portuguese [4].

In this paper we describe the Portuguese-language podcast dataset we have released for academic research purposes. We give an overview of how the data was sampled, descriptive statistics over the collection, as well as information about the distribution over Brazilian and Portuguese dialects. We give results from experiments on multi-lingual summarization, showing that summarizing podcast transcripts can be performed well by a system supporting both English and Portuguese. We also show experiments on Portuguese podcast genre classification using text metadata. Combining this collection with previously released English-language collection opens up the potential for multi-modal, multi-lingual and multi-dialect podcast information access research.

State-of-the-art Spoken Language Identification (SLI) systems usually focus on tackling short audio clips, and thus their performance degrade drastically when applied to long-form audio, such as podcast, which poses peculiar challenges to existing SLI approaches due to its long duration and diverse content that frequently involves multiple speakers as well as various languages, topics, and speech styles. In this paper, we propose the first system to tackle SLI for long-form audio using podcast data by training a lightweight, multi-class feedforward neural classifier using speaker embeddings as input. We demonstrate that our approach can make inference on long audio input efficiently; furthermore, our system can handle long audio files with multiple speakers and can be further extended into utterance-level inference and code-switching detection, which is currently not covered by any existing SLI system.

Podcasts are conversational in nature and speaker changes are frequent-requiring speaker diarization for content understanding. We propose an unsupervised technique for speaker diarization without relying on language-specific components. The algorithm is overlap-aware and does not require information about the number of speakers. Our approach shows 79% improvement on purity scores (34% on F-score) against the Google Cloud Platform solution on podcast data.

The AudioCHI 2022 workshop focusses on human engagement with spoken material in search settings, including live stream audio and collections. Spoken material comes in many forms, including for example: factual or entertaining (or both!), timely or of historical interest, local or global, single speaker or conversations. Users engage with spoken material for a variety of reasons, including entertainment, current affairs, education, and research. While there has been considerable previous work studying spoken document retrieval or more generally spoken content retrieval, AudioCHI 2022 is the first meeting to explore user engagement with audio content, including discussing: (i) how content analysis might establish verbal and non-verbal features for rich content representations, and (ii) and use cases and human factors in interaction with spoken audio content, and their interaction with more established topics relating to spoken content retrieval. The workshop brings together researchers in spoken content retrieval with expertise on human computer interaction in information access to examine opportunities and challenges for advancing technologies for search and interaction with spoken content.

In this work, we study the association between song lyrics and mood through a data-driven analysis. Our data set consists of nearly one million songs, with song-mood associations derived from user playlists on the Spotify streaming platform. We take advantage of state-of-the-art natural language processing models based on transformers to learn the association between the lyrics and moods. We find that a pretrained transformer-based language model in a zero-shot setting -- i.e., out of the box with no further training on our data -- is powerful for capturing song-mood associations. Moreover, we illustrate that training on song-mood associations results in a highly accurate model that predicts these associations for unseen songs. Furthermore, by comparing the prediction of a model using lyrics with one using acoustic features, we observe that the relative importance of lyrics for mood prediction in comparison with acoustics depends on the specific mood. Finally, we verify if the models are capturing the same information about lyrics and acoustics as humans through an annotation task where we obtain human judgments of mood-song relevance based on lyrics and acoustics.

. This document describes the Portuguese language podcast dataset released by Spotify for academic research purposes. We give an overview of how the data was sampled, some basic statistics over the collection, as well as brief information of distribution over Brazilian and Portuguese dialects.

Abstractive summarization of podcasts is motivated by the growing popularity of podcasts and the needs of their listeners. Podcasting is a markedly different domain from news and other media that are commonly studied in the context of automatic summarization. As such, the qualities of a good podcast summary are yet unknown. Using a collection of podcast summaries produced by different algorithms alongside human judgments of summary quality obtained from the TREC 2020 Podcasts Track, we study the correlations between various automatic evaluation metrics and human judgments, as well as the linguistic aspects of summaries that result in strong evaluations.

The AudioCHI 2022 workshop focusses on human engagement with spoken material in search settings, including live stream audio and collections. Spoken material comes in many forms, including for example: factual or entertaining (or both!), timely or of historical interest, local or global, single speaker or conversations. Users engage with spoken material for a variety of reasons, including entertainment, current affairs, education, and research. While there has been considerable previous work studying spoken document retrieval or more generally spoken content retrieval, AudioCHI 2022 is the first meeting to explore user engagement with audio content, including discussing: (i) how content analysis might establish verbal and non-verbal features for rich content representations, and (ii) and use cases and human factors in interaction with spoken audio content, and their interaction with more established topics relating to spoken content retrieval. The workshop brings together researchers in spoken content retrieval with expertise on human computer interaction in information access to examine opportunities and challenges for advancing technologies for search and interaction with spoken content.

This is a report from the AudioCHI 2022 workshop which was organised in conjunction with the CHIIR conference in March 2022. The workshop was organised to bring together researchers in spoken content retrieval with expertise on human computer interaction in information access to examine opportunities and challenges for advancing technologies for search and interaction with spoken content. The workshop was originally planned to be a full day event, but due to pandemic-imposed travelling constraints, it was instead held as a virtual half day event to accommodate time zone differences for participants. Date: 14 March, 2022. Website: https://speechretrievalworkshop.github.io/.

Podcast episodes often contain material extraneous to the main content, such as advertisements, interleaved within the audio and the written descriptions. We present classifiers that leverage both textual and listening patterns in order to detect such content in podcast descriptions and audio transcripts. We demonstrate that our models are effective by evaluating them on the downstream task of podcast summarization and show that we can substantively improve ROUGE scores and reduce the extraneous content generated in the summaries.

Podcasts have continued to experience rapid growth in both cultural relevance as well as research attention. Coming off the success of the first PodRecs Workshop for Podcast Recommendations at RecSys in 2020, as well as to build upon the research datasets and prior work released in the last year, the second PodRecs Workshop for Podcast Recommendations was held at RecSys 2021 to further develop the community of researchers and practitioners interested in the recommendation of podcasts.

Rapidly growing online podcast archives contain diverse content on a wide range of topics. These archives form an important resource for entertainment and professional use, but their value can only be realized if users can rapidly and reliably locate content of interest. Search for relevant content can be based on metadata provided by content creators, but also on transcripts of the spoken content itself. Excavating relevant content from deep within these audio streams for diverse types of information needs requires varying the approach to systems prototyping. We describe a set of diverse podcast information needs and different approaches to assessing retrieved content for relevance. We use these information needs in an investigation of the utility and effectiveness of these information sources. Based on our analysis, we recommend approaches for indexing and retrieving podcast content for ad hoc search.

The TREC Podcasts Track is intended to facilitate research in language technologies applied to podcasts specifically by lowering the barrier-to-entry for data-oriented research for podcasts and for diverse spoken documents in general. This year, 2021, is the second year of the track. A more general overview of some of the challenges is given in last year’s Podcasts Track Overview. The track this year consisted of two shared tasks: segment retrieval and summarisation , both based on a dataset of over 100,000 podcast episodes (metadata, audio, and automatic transcripts) which was released concurrently with the track. The tasks were slightly elaborated this year to encourage participants to use audio analysis. This paper gives an overview of the tasks and the results of the participants’ experiments.

Podcasts are spoken documents across a wide-range of genres and styles, with growing listenership across the world, and a rapidly lowering barrier to entry for both listeners and creators. The great strides in search and recommendation in research and industry have yet to see impact in the podcast space, where recommendations are still largely driven by word of mouth. In this perspective paper, we highlight the many differences between podcasts and other media, and discuss our perspective on challenges and future research directions in the domain of podcast information access.

While there is an abundance of popular writing targeted to podcast creators on how to speak in ways that engage their listeners, there has been little data-driven analysis of podcasts that relates linguistic style with listener engagement. In this paper, we investigate how various factors -- vocabulary diversity, distinctiveness, emotion, and syntax, among others -- correlate with engagement, based on analysis of the creators' written descriptions and transcripts of the audio. We build models with different textual representations, and show that the identified features are highly predictive of engagement. Our analysis tests popular wisdom about stylistic elements in high-engagement podcasts, corroborating some aspects, and adding new perspectives on others.

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.