Faculty of Engineering, Multimedia University, Cyberjaya 63100, Malaysia.
Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Malaysia.
Sensors (Basel). 2021 Jan 21;21(3):710. doi: 10.3390/s21030710.
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.
鉴于音频和视频文件中存在过多的脏话，以及这些脏话对个人性格和行为的不利影响，对内容进行审查以过滤掉年轻观众接触到的未经过滤的内容中的脏话至关重要。虽然实施了手动检测和审查，但这些方法被证明很繁琐。不可避免地，由于人类疲劳和人类视觉系统在长时间筛选时的性能较低，会出现涉及脏话的错误识别。因此，本文提出了一种通过使用先进的深度卷积神经网络（CNN）和循环神经网络（RNN）以及长短期记忆（LSTM）单元的机械化和强大检测方法，实现脏话审查的智能系统。对脏话数据进行了收集、标注、扩充和分析，以开发和评估 CNN 和 RNN 配置。因此，报告的高比例咒骂词识别结果（假阴性率仅为 2.53%至 5.92%）表明了所提出系统的可行性。所提出的系统在新颖的脏话数据集上优于最先进的预训练神经网络，并证明可以通过最小化可训练参数来降低计算成本。
