Acta Marisiensis.
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Anul 2024
Volum 21 (XXXVIII), nr 1 Volum 21 (XXXVIII), nr 2 Anul 2023 Volum 20 (XXXVII), nr 1 Volum 20 (XXXVII), nr 2 Anul 2022 Volum 19 (XXXVI), nr 1 Volum 19 (XXXVI), nr 2 Anul 2021 Volum 18 (XXXV), nr 1 Volum 18 (XXXV), nr 2 Anul 2020 Volum 17 (XXXIV), nr 1 Volum 17 (XXXIV), nr 2 Anul 2019 Volum 16 (XXXIII), nr 1 Volum 16 (XXXIII), nr 2 Anul 2018 Volum 15 (XXXII), nr 1 Volum 15 (XXXII), nr 2 Anul 2017 Volum 14 (XXXI), nr 1 Volum 14 (XXXI), nr 2 Anul 2016 Volum 13 (XXX), nr 1 Volum 13 (XXX), nr 2 Anul 2015 Volum 12 (XXIX), nr 1 Volum 12 (XXIX), nr 2 Anul 2014 Volum 11 (XXVIII), nr 1 Volum 11 (XXVIII), nr 2 Anul 2013 Volum 10 (XXVII), nr 1 Volum 10 (XXVII), nr 2 Anul 2012 Volum 9 (XXVI), nr 1 Volum 9 (XXVI), nr 2 Anul 2011 Volum 8 (XXV), nr 1 Volum 8 (XXV), nr 2 Anul 2010 Volum 7 (XXIV), nr 1 Volum 7 (XXIV), nr 2 Anul 2009 Volum 6 (XXIII) |
2023, Volume 20 (XXXVII), no 2
Zsuzsa SIMÓ, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures Abstract: The paper shows the understanding of a topic recognition problem like the speech recognition system based on Natural Language Processing (NLP) and the steps of its implementation of a rules-based approach, which is able to classify given audio materials based on predefined topics in real-time. During implementation, a statistical vocabulary was developed. Google Speech API (Application Programming Interface) was employed for subtitling audio materials, and the most ideal time frame for reception was identified through several experiments. The motivation of this work is based on the deficiency of similar simple systems for Hungarian topic recognition, even though numerous international languages already utilize multiple Automatic Sound Recognition (ASR) systems. DOI: https://doi.org/10.2478/amset-2023-0017 Pages: 43-48 Cite as: download info as bibtex View full article |
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Update: 18-Dec-2024 | © Published by University Press |