Abstract
Automatic speech recognition involves an understanding of what is being said. It can be audio-based, visual-based, or audio/visual-based according to the type of inputs. Modern speech recognition systems are based on machine learning techniques, such as deep learning. Deep learning systems improve their performance when more data are used to train them. Therefore, data has become one of the most valuable assets in the field of Artificial Intelligence. In this work, we present a methodology to create a database for audio/visual speech recognition. Due to the lack of Spanish datasets, we created a comprehensive Spanish-based speech recognition dataset. For this, we selected hundreds of YouTube videos, found the facial features, and aligned the voice beside text with millisecond accuracy using IBM speech-to-text technology. We split the data into three speaker face angles, where the frontal angle represents the simple case, and right-left angles represent harder cases. As a result, we obtained a dataset of more than 100 thousand samples consisting of a small video with its respective annotation. Our approach can be used to generate datasets on any language by merely selecting videos in the desired language. The database and the source code to create it are open-source.
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Acknowledgements
The authors wish to acknowledge the support for this work to Universidad Autónoma de Querétaro (UAQ) through project FIF-2018-06.
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Córdova-Esparza, DM., Terven, J., Romero, A., Herrera-Navarro, A.M. (2019). Audio-Visual Database for Spanish-Based Speech Recognition Systems. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_36
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