International Journal of Science, Engineering and TechnologyAuthors: Om Agarwal, Dr. Jasbir Kaur, Assistant Professor Ifrah Kampoo
Abstract: This paper presents CleanTalk, a Natural Language Processing (NLP)-driven system designed to automate the detection and severity assessment of profane language within video content. The system operates by extracting audio tracks from video files, transcribing the resulting audio into textual form using speech-to-text technology, and subsequently applying NLP-based analytical techniques to identify profane words and evaluate their severity. Each detected instance is annotated with a corresponding timestamp, and the system produces an overall severity score summarizing the nature of the language throughout the content. The final output consists of an annotated video file with visual markers at profanity timestamps alongside a structured severity report. A pilot evaluation was conducted across three video samples of varying content type, yielding an average precision of 96.7% and an average recall of 84.5%, with performance degradation observed primarily in multi-speaker and fast-speech scenarios. CleanTalk demonstrates strong potential as a scalable, automated solution for content moderation workflows on digital platforms.
