There is a newer version of the record available.

Published May 28, 2025 | Version 1.0
Dataset Restricted

AITW: The Annotated In-the-Wild Dataset for Filtering of In-the-Wild Speech Data

Authors/Creators

  • 1. ConnexAI

Description

The Annotated In-The-Wild (AITW) Dataset for Filtering of In-the-Wild Speech Data (v1.0 - Superseded)

! PLEASE USE THE MOST RECENT VERSION OF THIS DATASET FOUND HERE: https://zenodo.org/records/16937418

The Annotated In-The-Wild (AITW) dataset accompanies the paper “Whilter: A Whisper-based Data Filter for ‘in-the-wild’ Speech Corpora Using Utterance-level Multi-Task Classification”, accepted at Interspeech 2025 in Rotterdam. This dataset supports research into automated filtering of noisy or undesirable audio segments in large-scale, real-world speech corpora, particularly for training high-quality English TTS and ASR models.

AITW includes over 21,000 manually labeled audio samples (≈64 hours) from two popular in-the-wild speech datasets (Emilia and YODAS). Each audio clip is annotated at the utterance level with binary or numerical labels for five key properties.

Numerical labels:

  • Speaker count

Binary labels:

  • Non-English (foreign) language
  • Background music
  • Noisy or poor-quality speech
  • Synthetic (spoofed) speech

Annotations were performed by expert annotators using a custom Label Studio interface, with consistent guidelines applied across all tasks. This dataset enables the benchmarking of multi-task classification models like Whilter and comparison with single-task baselines.

AITW is designed to foster further research in scalable speech data curation and low-resource dataset bootstrapping. We encourage contributions and improvements through the included Label Studio GUI.

Files include:

  • Labeled audio metadata along with file maps which map back to the data in YODAS and Emilia (.csv or .json)
  • Interface config for Label Studio (.xml)

If you use this dataset, please cite:

W. Ravenscroft, G. Close, K. Bower-Morris, J. Stacey, D. Sityaev, K. Hong. “Whilter: A Whisper-based Data Filter for ‘in-the-wild’ Speech Corpora Using Utterance-level Multi-Task Classification,” Interspeech 2025.

License:

Creative Commons Attribution 4.0 International (CC BY 4.0)

 

Files

Restricted

The record is publicly accessible, but files are restricted. Log in to check if you have access.

Additional details

Dates

Accepted
2025-05-19
Interspeech 2025

Software

Development Status
Active

References

  • X. Li, S. Takamichi, T. Saeki, W. Chen, S. Shiota and S. Watanabe, "Yodas: Youtube-Oriented Dataset for Audio and Speech," 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), Taipei, Taiwan, 2023, pp. 1-8, doi: 10.1109/ASRU57964.2023.10389689. keywords: {Training;Video on demand;Conferences;Pipelines;Self-supervised learning;Manuals;Data collection;multilingual speech processing;speech recognition;large-scale speech dataset},
  • H. He et al., "Emilia: An Extensive, Multilingual, and Diverse Speech Dataset For Large-Scale Speech Generation," 2024 IEEE Spoken Language Technology Workshop (SLT), Macao, 2024, pp. 885-890, doi: 10.1109/SLT61566.2024.10832365. keywords: {Training;Technological innovation;Annotations;Conferences;Pipelines;Training data;Transforms;Speech;Data models;Multilingual;Extensive Multilingual and Diverse Dataset;Large-scale Speech Generation},