Acta Marisiensis.
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Year 2024
Volume 21 (XXXVIII), no 1 Year 2023 Volume 20 (XXXVII), no 1 Volume 20 (XXXVII), no 2 Year 2022 Volume 19 (XXXVI), no 1 Volume 19 (XXXVI), no 2 Year 2021 Volume 18 (XXXV), no 1 Volume 18 (XXXV), no 2 Year 2020 Volume 17 (XXXIV), no 1 Volume 17 (XXXIV), no 2 Year 2019 Volume 16 (XXXIII), no 1 Volume 16 (XXXIII), no 2 Year 2018 Volume 15 (XXXII), no 1 Volume 15 (XXXII), no 2 Year 2017 Volume 14 (XXXI), no 1 Volume 14 (XXXI), no 2 Year 2016 Volume 13 (XXX), no 1 Volume 13 (XXX), no 2 Year 2015 Volume 12 (XXIX), no 1 Volume 12 (XXIX), no 2 Year 2014 Volume 11 (XXVIII), no 1 Volume 11 (XXVIII), no 2 Year 2013 Volume 10 (XXVII), no 1 Volume 10 (XXVII), no 2 Year 2012 Volume 9 (XXVI), no 1 Volume 9 (XXVI), no 2 Year 2011 Volume 8 (XXV), no 1 Volume 8 (XXV), no 2 Year 2010 Volume 7 (XXIV), no 1 Volume 7 (XXIV), no 2 Year 2009 Volume 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 View full article |
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Update: 19-Jun-2024 | © Published by University Press |