Design and implementation of fast spoken foul language recognition with different end-to-end deep neural network architectures

Abdulaziz Saleh Ba Wazir, Hezerul Abdul Karim, Mohd Haris Lye Abdullah, Nouar Aldahoul, Sarina Mansor, Mohammad Faizal Ahmad Fauzi, John See, Ahmad Syazwan Naim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
12 Downloads (Pure)


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.

Original languageEnglish
Article number710
Number of pages17
Issue number3
Publication statusPublished - 21 Jan 2021


  • Censorship
  • Convolutional neural networks
  • Deep learning
  • Foul language
  • Long short-term memory
  • Recurrent neural networks
  • Speech recognition

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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