Data Descriptor
Version 1
Preserved in Portico This version is not peer-reviewed
Visual Lip Reading Dataset in Turkish
Version 1
: Received: 7 December 2022 / Approved: 7 December 2022 / Online: 7 December 2022 (06:50:33 CET)
A peer-reviewed article of this Preprint also exists.
Berkol, A.; Tümer-Sivri, T.; Pervan-Akman, N.; Çolak, M.; Erdem, H. Visual Lip Reading Dataset in Turkish. Data 2023, 8, 15. Berkol, A.; Tümer-Sivri, T.; Pervan-Akman, N.; Çolak, M.; Erdem, H. Visual Lip Reading Dataset in Turkish. Data 2023, 8, 15.
Abstract
The promised dataset was obtained from the daily Turkish words and phrases pronounced by various people in the videos posted on YouTube. The purpose of collecting the dataset is to provide detection of the spoken word by recognizing patterns or classifying lip movements with supervised, unsupervised, semi-supervised learning and machine learning algorithms. Most of the datasets related with lip reading consist of people recorded on camera with fixed backgrounds and the same conditions, but the dataset presented here consists of images compatible with machine learning models developed for real-life challenges. It contains a total of 2335 instances taken from TV series, movies, vlogs, and song clips on YouTube. The images in the dataset vary due to factors such as the way people say words, accent, speaking rate, gender and age. Furthermore, the instances in the dataset consist of videos with different angles, shadows, resolution, and brightness that are not created manually. The most important feature of our lip reading dataset is that we contribute to the non-synthetic Turkish dataset pool, which does not have wide dataset varieties. Machine learning studies can be carried out in many areas, such as the defense industry and social life, with this dataset.
Keywords
Lip reading; Visual speech recognition; Turkish dataset; Face parts detection
Subject
Computer Science and Mathematics, Information Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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