Bengali Abusive Speech Classification: A Transfer Learning Approach Using VGG-16

Shantanu Kumar Rahut, Riffat Sharmin, Ridma Tabassum
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引用次数: 4

Abstract

Swear words used in speech to abuse someone is frowned upon in every society. Abusive speeches can destroy the victim's morale, mental strength, and the will to live. Abusing others through social media, video streaming sites, and over voice calls are becoming a common problem. There are laws to punish the offenders. However, without proper surveillance, stopping abusive speech is tough. Machine learning can help to create surveillance methods by detecting abusive speech from human conversation. There have been a few works in the relevant field to detect abusive speech. However, detecting abusive speech in the Bengali language remains an unexplored area. This paper aims at providing an approach towards the classification of abusive and non-abusive Bengali speech. The authors collected 960 voice recordings of native Bengali speakers. The authors used Transfer Learning for extracting features from the data. Then, the authors used different methods for classification. The proposed approach achieves high accuracy (98.61%) in classifying abusive and non-abusive Bengali speech.
孟加拉语辱骂语分类:基于VGG-16的迁移学习方法
在演讲中使用辱骂他人的脏话在任何社会都是不被允许的。辱骂性的演讲可以摧毁受害者的士气、精神力量和生存的意志。通过社交媒体、视频流媒体网站、语音通话等滥用他人的现象越来越普遍。有法律惩罚违法者。然而,如果没有适当的监督,阻止辱骂言论是困难的。机器学习可以通过检测人类对话中的辱骂性语言来帮助创建监视方法。在相关领域已经有了一些检测辱骂性言语的工作。然而,检测孟加拉语中的辱骂言语仍然是一个未开发的领域。本文旨在为孟加拉语的辱骂性和非辱骂性言语的分类提供一种方法。作者收集了960个以孟加拉语为母语的人的录音。作者使用迁移学习从数据中提取特征。然后,作者使用了不同的分类方法。该方法对孟加拉语辱骂性和非辱骂性言语的分类准确率高达98.61%。
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