Citation

Ba Wazir, Abdulaziz Saleh and Abdul Karim, Hezerul and Lye Abdullah, Mohd Haris and AlDahoul, Nouar and Ahmad Fauzi, Mohammad Faizal and See, John Su Yang and Naim, Ahmad Syazwan and Mansor, Sarina (2021) Design and implementation of fast spoken foul language recognition with different end-to-end deep neural network architectures. Sensors, 21 (3) (710). pp. 1-17. ISSN 1424-8220

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Abstract

Given the excessive foul language identified in audio and video files and the detrimental consequences to an individual’s character and behaviour, content censorship is crucial to filter profanities from young viewers with higher exposure to uncensored content. Although manual detection and censorship were implemented, the methods proved tedious. Inevitably, misidentifications involving foul language owing to human weariness and the low performance in human visual systems concerning long screening time occurred. As such, this paper proposed an intelligent system for foul language censorship through a mechanized and strong detection method using advanced deep Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) through Long Short-Term Memory (LSTM) cells. Data on foul language were collected, annotated, augmented, and analysed for the development and evaluation of both CNN and RNN configurations. Hence, the results indicated the feasibility of the suggested systems by reporting a high volume of curse word identifications with only 2.53% to 5.92% of False Negative Rate (FNR). The proposed system outperformed state-of-the-art pre-trained neural networks on the novel foul language dataset and proved to reduce the computational cost with minimal trainable parameters.

Item Type:	Article
Uncontrolled Keywords:	foul language, speech recognition, censorship, deep learning, convolutional neural networks, recurrent neural networks, long short-term memory
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics > TK7885-7895 Computer engineering. Computer hardware
Divisions:	Faculty of Computing and Informatics (FCI) Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Mar 2021 23:39
Last Modified:	07 Mar 2021 23:39
URII:	http://shdl.mmu.edu.my/id/eprint/8545

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