The PodcastFillers dataset consists of 199 full-length podcast episodes in English with manually annotated filler words and automatically generated transcripts. The podcast audio recordings, sourced from SoundCloud, are CC-licensed, gender-balanced, and total 145 hours of audio from over 350 speakers. The annotations are provided under a non-commercial license and consist of 85,803 manually annotated audio events including approximately 35,000 filler words (“uh” and “um”) and 50,000 non-filler events such as breaths, music, laughter, repeated words, and noise. The annotated events are also provided as pre-processed 1-second audio clips. The dataset also includes automatically generated speech transcripts from a speech-to-text system. A detailed description is provided in Dataset.
Variants: PodcastFillers
This dataset is used in 1 benchmark:
Task | Model | Paper | Date |
---|---|---|---|
Sound Event Localization and Detection | AVC-FillerNet | Filler Word Detection and Classification: … | 2022-03-28 |
Sound Event Localization and Detection | VC-FillerNet | Filler Word Detection and Classification: … | 2022-03-28 |
Recent papers with results on this dataset: