Spectrogram-Based Classification Of Spoken Foul Language Using Deep CNN


Ba Wazir, Abdulaziz Saleh and Abdul Karim, Hezerul and Lye Abdullah, Mohd Haris and Mansor, Sarina and AlDahoul, Nouar and Ahmad Fauzi, Mohammad Faizal and See, John Su Yang (2020) Spectrogram-Based Classification Of Spoken Foul Language Using Deep CNN. In: 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), 21-24 Sept. 2020, Tampere, Finland.

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Excessive content of profanity in audio and video files has proven to shape one’s character and behavior. Currently, conventional methods of manual detection and censorship are being used. Manual censorship method is time consuming and prone to misdetection of foul language. This paper proposed an intelligent model for foul language censorship through automated and robust detection by deep convolutional neural networks (CNNs). A dataset of foul language was collected and processed for the computation of audio spectrogram images that serve as an input to evaluate the classification of foul language. The proposed model was first tested for 2-class (Foul vs Normal) classification problem, the foul class is then further decomposed into a 10-class classification problem for exact detection of profanity. Experimental results show the viability of proposed system by demonstrating high performance of curse words classification with 1.24-2.71 Error Rate (ER) for 2-class and 5.49-8.30 F1-score. Proposed Resnet50 architecture outperforms other models in terms of accuracy, sensitivity, specificity, F1-score.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Neural networks (Computer science), Foul language, Speech detection, Censorship, Spectrogram, CNN.
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 10 Sep 2021 14:59
Last Modified: 10 Sep 2021 14:59
URII: http://shdl.mmu.edu.my/id/eprint/8516


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