# RadioTalk: a large-scale corpus of talk radio transcripts

arXiv: https://arxiv.org/abs/1907.07073 · HTML: https://mlsanity.com/paper/1907.07073/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate Users](https://mlsanity.com/paper/2104.13083/) (2021)
- [Toxicity Begets Toxicity: Unraveling Conversational Chains in Political Podcasts](https://mlsanity.com/paper/2501.12640/) (2025)
- [News Reporter: A Multi-lingual LLM Framework for Broadcast T.V News](https://mlsanity.com/paper/2410.07520/) (2024)
- [WavePulse: Real-time Content Analytics of Radio Livestreams](https://mlsanity.com/paper/2412.17998/) (2024)
- [A large-scale corpus of religious radio broadcast transcripts from webstream recordings in the United States](https://mlsanity.com/paper/2607.26249/) (2026)

## Abstract (arXiv)

We introduce RadioTalk, a corpus of speech recognition transcripts sampled from talk radio broadcasts in the United States between October of 2018 and March of 2019. The corpus is intended for use by researchers in the fields of natural language processing, conversational analysis, and the social sciences. The corpus encompasses approximately 2.8 billion words of automatically transcribed speech from 284,000 hours of radio, together with metadata about the speech, such as geographical location, speaker turn boundaries, gender, and radio program information. In this paper we summarize why and how we prepared the corpus, give some descriptive statistics on stations, shows and speakers, and carry out a few high-level analyses.