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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Helsinki Speech Challenge 2024 open audio dataset

Authors: Ludvigsen, Martin; Karvonen, Elli; Juvonen, Markus; Siltanen, Samuli;

Helsinki Speech Challenge 2024 open audio dataset

Abstract

Dataset This training dataset is originally designed for the Helsinki Speech Challenge 2024 (HSC2024). While it was created with this challenge in mind, its applications extend far beyond, making it a valuable resource for developing and testing audio algorithms across diverse uses. Our dataset features clean speech samples generated by OpenAI's text-to-speech model, paired with corresponding recorded signals. These recorded signals are purposefully distorted by real-world effects such as filtering and reverb, offering a realistic testing ground for your audio processing algorithms. To ensure ease of use, the audio samples are organized into 10 separate zip files, each dedicated to a specific task and level of complexity. Most zip files include two folders clean and recorded: one containing clean audio data and the other housing the corresponding recorded (distorted) data. However, note that folders Task_3_Level_1 and Task_3_Level_2 only include recorded data, as their clean counterparts are identical to those in Task_2_Level_2 and Task_2_Level_3, respectively. Additionally, each zip file includes a .txt file with the original text samples associated with the audio clips. Additionally, there is folder Impulse_Responses, which contains a clean sine sweep signal and short and a long white noise signal with recorded counterparts. To help you get started, we've also provided an example folder containing samples from each task and level, giving you a comprehensive overview of the dataset's scope and variety.The dataset also contains a python script evaluate.py. This can be used to evaluate the quality of audio files using the Mozilla Deepspeech speech recognition model. For more details on this script, see the more detailed description of the data challenge either on the website below or on arXiv https://arxiv.org/abs/2406.04123.Important dates: Data Challenge Launch: 10. June 2024. Sign-up deadline: 1. September 2024 (if you missed this deadline and wish to participate in the challenge, please send us an email). Submission deadline: 6. October 2024. We realize this deadline is a bit optimistic, but we humbly ask participants to try to make this deadline. Results are published: 4. November. Inverse days: 10.-13. December in Oulu, Finland. Here is a link to the official webpage of the HSC2024: https://blogs.helsinki.fi/helsinki-speech-challenge/Contact Email: hsc2024@helsinki.fi

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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